--- _id: '45884' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo - first_name: Mohamed full_name: Sherif, Mohamed id: '67234' last_name: Sherif orcid: https://orcid.org/0000-0002-9927-2203 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104. doi:10.5281/zenodo.8068466' apa: Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466 bibtex: '@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Configuration and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466}, booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }' chicago: 'Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration and Evaluation.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.5281/zenodo.8068466.' ieee: 'J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.' mla: Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:10.5281/zenodo.8068466. short: 'J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A. Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 85–104.' date_created: 2023-07-07T07:50:53Z date_updated: 2023-07-07T11:20:12Z ddc: - '040' department: - _id: '7' doi: 10.5281/zenodo.8068466 editor: - first_name: Claus-Jochen full_name: Haake, Claus-Jochen last_name: Haake - first_name: Friedhelm full_name: Meyer auf der Heide, Friedhelm last_name: Meyer auf der Heide - first_name: Marco full_name: Platzner, Marco last_name: Platzner - first_name: Henning full_name: Wachsmuth, Henning last_name: Wachsmuth - first_name: Heike full_name: Wehrheim, Heike last_name: Wehrheim file: - access_level: open_access content_type: application/pdf creator: florida date_created: 2023-07-07T07:50:34Z date_updated: 2023-07-07T11:20:11Z file_id: '45885' file_name: B2-Chapter-SFB-Buch-Final.pdf file_size: 895091 relation: main_file file_date_updated: 2023-07-07T11:20:11Z has_accepted_license: '1' intvolume: ' 412' language: - iso: eng oa: '1' page: 85-104 place: Paderborn project: - _id: '1' grant_number: '160364472' name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' grant_number: '160364472' name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)' publication: On-The-Fly Computing -- Individualized IT-services in dynamic markets publisher: Heinz Nixdorf Institut, Universität Paderborn series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts status: public title: Configuration and Evaluation type: book_chapter user_id: '477' volume: 412 year: '2023' ... --- _id: '45863' abstract: - lang: eng text: "In the proposal for our CRC in 2011, we formulated a vision of markets for\r\nIT services that describes an approach to the provision of such services\r\nthat was novel at that time and, to a large extent, remains so today:\r\n„Our vision of on-the-fly computing is that of IT services individually and\r\nautomatically configured and brought to execution from flexibly combinable\r\nservices traded on markets. At the same time, we aim at organizing\r\nmarkets whose participants maintain a lively market of services through\r\nappropriate entrepreneurial actions.“\r\nOver the last 12 years, we have developed methods and techniques to\r\naddress problems critical to the convenient, efficient, and secure use of\r\non-the-fly computing. Among other things, we have made the description\r\nof services more convenient by allowing natural language input,\r\nincreased the quality of configured services through (natural language)\r\ninteraction and more efficient configuration processes and analysis\r\nprocedures, made the quality of (the products of) providers in the\r\nmarketplace transparent through reputation systems, and increased the\r\nresource efficiency of execution through reconfigurable heterogeneous\r\ncomputing nodes and an integrated treatment of service description and\r\nconfiguration. We have also developed network infrastructures that have\r\na high degree of adaptivity, scalability, efficiency, and reliability, and\r\nprovide cryptographic guarantees of anonymity and security for market\r\nparticipants and their products and services.\r\nTo demonstrate the pervasiveness of the OTF computing approach, we\r\nhave implemented a proof-of-concept for OTF computing that can run\r\ntypical scenarios of an OTF market. We illustrated the approach using\r\na cutting-edge application scenario – automated machine learning (AutoML).\r\nFinally, we have been pushing our work for the perpetuation of\r\nOn-The-Fly Computing beyond the SFB and sharing the expertise gained\r\nin the SFB in events with industry partners as well as transfer projects.\r\nThis work required a broad spectrum of expertise. Computer scientists\r\nand economists with research interests such as computer networks and\r\ndistributed algorithms, security and cryptography, software engineering\r\nand verification, configuration and machine learning, computer engineering\r\nand HPC, microeconomics and game theory, business informatics\r\nand management have successfully collaborated here." alternative_title: - Collaborative Research Centre 901 (2011 – 2023) author: - first_name: Claus-Jochen full_name: Haake, Claus-Jochen id: '20801' last_name: Haake - first_name: Friedhelm full_name: Meyer auf der Heide, Friedhelm id: '15523' last_name: Meyer auf der Heide - first_name: Marco full_name: Platzner, Marco id: '398' last_name: Platzner - first_name: Henning full_name: Wachsmuth, Henning id: '3900' last_name: Wachsmuth - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Heinz Nixdorf Institut, Universität Paderborn; 2023. doi:10.17619/UNIPB/1-1797 apa: Haake, C.-J., Meyer auf der Heide, F., Platzner, M., Wachsmuth, H., & Wehrheim, H. (2023). On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.17619/UNIPB/1-1797 bibtex: '@book{Haake_Meyer auf der Heide_Platzner_Wachsmuth_Wehrheim_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, volume={412}, DOI={10.17619/UNIPB/1-1797}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }' chicago: 'Haake, Claus-Jochen, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol. 412. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.17619/UNIPB/1-1797.' ieee: 'C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.' mla: Haake, Claus-Jochen, et al. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Heinz Nixdorf Institut, Universität Paderborn, 2023, doi:10.17619/UNIPB/1-1797. short: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim, On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023. date_created: 2023-07-05T07:16:51Z date_updated: 2023-08-29T06:44:36Z ddc: - '000' department: - _id: '7' doi: 10.17619/UNIPB/1-1797 file: - access_level: open_access content_type: application/pdf creator: ups date_created: 2023-07-05T07:15:55Z date_updated: 2023-07-05T07:19:14Z file_id: '45864' file_name: SFB-Buch-Final.pdf file_size: 15480050 relation: main_file file_date_updated: 2023-07-05T07:19:14Z has_accepted_license: '1' intvolume: ' 412' language: - iso: eng oa: '1' page: '247' place: Paderborn project: - _id: '1' grant_number: '160364472' name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ' - _id: '2' name: 'SFB 901 - A: SFB 901 - Project Area A' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '82' name: 'SFB 901 - T: SFB 901 - Project Area T' - _id: '5' grant_number: '160364472' name: 'SFB 901 - A1: SFB 901 - Möglichkeiten und Grenzen lokaler Strategien in dynamischen Netzen (Subproject A1)' - _id: '7' grant_number: '160364472' name: 'SFB 901 - A3: SFB 901 - Der Markt für Services: Anreize, Algorithmen, Implementation (Subproject A3)' - _id: '8' grant_number: '160364472' name: 'SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen (Subproject A4)' - _id: '9' grant_number: '160364472' name: 'SFB 901 - B1: SFB 901 - Parametrisierte Servicespezifikation (Subproject B1)' - _id: '10' grant_number: '160364472' name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)' - _id: '11' name: 'SFB 901 - B3: SFB 901 - Subproject B3' - _id: '12' name: 'SFB 901 - B4: SFB 901 - Subproject B4' - _id: '13' name: 'SFB 901 - C1: SFB 901 - Subproject C1' - _id: '14' grant_number: '160364472' name: 'SFB 901 - C2: SFB 901 - On-The-Fly Compute Centers I: Heterogene Ausführungsumgebungen (Subproject C2)' - _id: '16' grant_number: '160364472' name: 'SFB 901 - C4: SFB 901 - On-The-Fly Compute Centers II: Ausführung komponierter Dienste in konfigurierbaren Rechenzentren (Subproject C4)' - _id: '17' name: 'SFB 901 - C5: SFB 901 - Subproject C5' - _id: '83' name: 'SFB 901 - T1: SFB 901 -Subproject T1' - _id: '84' name: 'SFB 901 - T2: SFB 901 -Subproject T2' publication_identifier: unknown: - 978-3-947647-31-6 publisher: Heinz Nixdorf Institut, Universität Paderborn series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts status: public title: On-The-Fly Computing -- Individualized IT-services in dynamic markets type: book user_id: '477' volume: 412 year: '2023' ... --- _id: '30868' abstract: - lang: eng text: "Algorithm configuration (AC) is concerned with the automated search of the\r\nmost suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently a wide variety of AC problem variants and methods proposed in the\r\nliterature. Existing reviews do not take into account all derivatives of the AC\r\nproblem, nor do they offer a complete classification scheme. To this end, we\r\nintroduce taxonomies to describe the AC problem and features of configuration\r\nmethods, respectively. We review existing AC literature within the lens of our\r\ntaxonomies, outline relevant design choices of configuration approaches,\r\ncontrast methods and problem variants against each other, and describe the\r\nstate of AC in industry. Finally, our review provides researchers and\r\npractitioners with a look at future research directions in the field of AC." author: - first_name: Elias full_name: Schede, Elias last_name: Schede - first_name: Jasmin full_name: Brandt, Jasmin last_name: Brandt - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Kevin full_name: Tierney, Kevin last_name: Tierney citation: ama: Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. arXiv:220201651. Published online 2022. apa: Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In arXiv:2202.01651. bibtex: '@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651}, author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022} }' chicago: Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022. ieee: E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022. mla: Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022. short: E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K. Tierney, ArXiv:2202.01651 (2022). date_created: 2022-04-12T12:00:08Z date_updated: 2022-04-12T12:01:15Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2202.01651' language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: arXiv:2202.01651 status: public title: A Survey of Methods for Automated Algorithm Configuration type: preprint user_id: '38209' year: '2022' ... --- _id: '34103' abstract: - lang: eng text: "It is well known that different algorithms perform differently well on an\r\ninstance of an algorithmic problem, motivating algorithm selection (AS): Given\r\nan instance of an algorithmic problem, which is the most suitable algorithm to\r\nsolve it? As such, the AS problem has received considerable attention resulting\r\nin various approaches - many of which either solve a regression or ranking\r\nproblem under the hood. Although both of these formulations yield very natural\r\nways to tackle AS, they have considerable weaknesses. On the one hand,\r\ncorrectly predicting the performance of an algorithm on an instance is a\r\nsufficient, but not a necessary condition to produce a correct ranking over\r\nalgorithms and in particular ranking the best algorithm first. On the other\r\nhand, classical ranking approaches often do not account for concrete\r\nperformance values available in the training data, but only leverage rankings\r\ncomposed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon\r\nforeSts - a new algorithm selector leveraging special forests, combining the\r\nstrengths of both approaches while alleviating their weaknesses. HARRIS'\r\ndecisions are based on a forest model, whose trees are created based on splits\r\noptimized on a hybrid ranking and regression loss function. As our preliminary\r\nexperimental study on ASLib shows, HARRIS improves over standard algorithm\r\nselection approaches on some scenarios showing that combining ranking and\r\nregression in trees is indeed promising for AS." author: - first_name: Lukass full_name: Fehring, Lukass last_name: Fehring - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede citation: ama: 'Fehring L, Hanselle JM, Tornede A. HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022. ; 2022.' apa: 'Fehring, L., Hanselle, J. M., & Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore.' bibtex: '@inproceedings{Fehring_Hanselle_Tornede_2022, title={HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection}, booktitle={Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022}, author={Fehring, Lukass and Hanselle, Jonas Manuel and Tornede, Alexander}, year={2022} }' chicago: 'Fehring, Lukass, Jonas Manuel Hanselle, and Alexander Tornede. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” In Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.' ieee: 'L. Fehring, J. M. Hanselle, and A. Tornede, “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection,” presented at the Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore, 2022.' mla: 'Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.' short: 'L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.' conference: location: Baltimore name: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022 date_created: 2022-11-17T12:57:40Z date_updated: 2022-11-17T13:00:53Z external_id: arxiv: - '2210.17341' language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022 status: public title: 'HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection' type: conference user_id: '38209' year: '2022' ... --- _id: '31806' abstract: - lang: eng text: The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds author: - first_name: Kevin full_name: Dreßler, Kevin id: '78256' last_name: Dreßler - first_name: Mohamed full_name: Sherif, Mohamed id: '67234' last_name: Sherif - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'Dreßler K, Sherif M, Ngonga Ngomo A-C. ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. In: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. ; 2022. doi:10.1145/3511095.3531287' apa: 'Dreßler, K., Sherif, M., & Ngonga Ngomo, A.-C. (2022). ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning. Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia. HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain). https://doi.org/10.1145/3511095.3531287' bibtex: '@inproceedings{Dreßler_Sherif_Ngonga Ngomo_2022, title={ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}, DOI={10.1145/3511095.3531287}, booktitle={Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}, author={Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2022} }' chicago: Dreßler, Kevin, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” In Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022. https://doi.org/10.1145/3511095.3531287. ieee: 'K. Dreßler, M. Sherif, and A.-C. Ngonga Ngomo, “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning,” presented at the HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain), 2022, doi: 10.1145/3511095.3531287.' mla: Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.” Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022, doi:10.1145/3511095.3531287. short: 'K. Dreßler, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022.' conference: end_date: 2022-07-01 location: Barcelona (Spain) name: 'HT ’22: 33rd ACM Conference on Hypertext and Social Media' start_date: 2022-06-28 date_created: 2022-06-08T08:47:33Z date_updated: 2022-11-18T10:11:38Z ddc: - '000' department: - _id: '34' doi: 10.1145/3511095.3531287 keyword: - 2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia status: public title: ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning type: conference user_id: '477' year: '2022' ... --- _id: '30867' abstract: - lang: eng text: "In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass are presented to an agent one after another, and the agent has to quickly\r\nselect a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence, machine learning methods used to optimize\r\nan algorithm selection strategy in a data-driven manner need to deal with\r\nright-censored samples, a problem that has received little attention in the\r\nliterature so far. In this work, we revisit multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses, allowing for partially censored\r\ndata while keeping a space- and time-complexity independent of the time\r\nhorizon. In an extensive experimental evaluation on an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically well-founded methods based\r\non Thompson sampling perform specifically strong and improve in comparison to\r\nexisting methods." author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial Intelligence. Published online 2022. apa: Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI. bibtex: '@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }' chicago: Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022. ieee: A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022. mla: Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI, 2022. short: A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022). date_created: 2022-04-12T11:58:56Z date_updated: 2022-08-24T12:44:27Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2109.06234' language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence publisher: AAAI status: public title: Machine Learning for Online Algorithm Selection under Censored Feedback type: preprint user_id: '38209' year: '2022' ... --- _id: '30865' abstract: - lang: eng text: "The problem of selecting an algorithm that appears most suitable for a\r\nspecific instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability problem, is called instance-specific algorithm selection. Over\r\nthe past decade, the problem has received considerable attention, resulting in\r\na number of different methods for algorithm selection. Although most of these\r\nmethods are based on machine learning, surprisingly little work has been done\r\non meta learning, that is, on taking advantage of the complementarity of\r\nexisting algorithm selection methods in order to combine them into a single\r\nsuperior algorithm selector. In this paper, we introduce the problem of meta\r\nalgorithm selection, which essentially asks for the best way to combine a given\r\nset of algorithm selectors. We present a general methodological framework for\r\nmeta algorithm selection as well as several concrete learning methods as\r\ninstantiations of this framework, essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors can significantly outperform single\r\nalgorithm selectors and have the potential to form the new state of the art in\r\nalgorithm selection." author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Lukas full_name: Gehring, Lukas last_name: Gehring - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. Machine Learning. Published online 2022. apa: Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In Machine Learning. bibtex: '@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2022} }' chicago: Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning, 2022. ieee: A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022. mla: Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine Learning, 2022. short: A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022). date_created: 2022-04-12T11:55:18Z date_updated: 2022-08-24T12:45:39Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2107.09414' language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: Machine Learning status: public title: Algorithm Selection on a Meta Level type: preprint user_id: '38209' year: '2022' ... --- _id: '33090' abstract: - lang: eng text: 'AbstractHeated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality.' author: - first_name: Karina full_name: Gevers, Karina id: '83151' last_name: Gevers - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Volker full_name: Schöppner, Volker id: '20530' last_name: Schöppner - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. Published online 2022. doi:10.1007/s40194-022-01339-9 apa: Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. https://doi.org/10.1007/s40194-022-01339-9 bibtex: '@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}, DOI={10.1007/s40194-022-01339-9}, journal={Welding in the World}, publisher={Springer Science and Business Media LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }' chicago: Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner, and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, 2022. https://doi.org/10.1007/s40194-022-01339-9. ieee: 'K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,” Welding in the World, 2022, doi: 10.1007/s40194-022-01339-9.' mla: Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9. short: K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022). date_created: 2022-08-24T12:51:07Z date_updated: 2022-08-24T12:52:06Z doi: 10.1007/s40194-022-01339-9 keyword: - Metals and Alloys - Mechanical Engineering - Mechanics of Materials language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: Welding in the World publication_identifier: issn: - 0043-2288 - 1878-6669 publication_status: published publisher: Springer Science and Business Media LLC status: public title: A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials type: journal_article user_id: '38209' year: '2022' ... --- _id: '28350' abstract: - lang: eng text: "In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks).\r\nIn this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software\r\ndiscrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime" author: - first_name: Arnab full_name: Sharma, Arnab id: '67200' last_name: Sharma - first_name: Caglar full_name: Demir, Caglar id: '43817' last_name: Demir - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Sharma A, Demir C, Ngonga Ngomo A-C, Wehrheim H. MLCHECK–Property-Driven Testing of Machine Learning Classifiers. In: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE.' apa: Sharma, A., Demir, C., Ngonga Ngomo, A.-C., & Wehrheim, H. (n.d.). MLCHECK–Property-Driven Testing of Machine Learning Classifiers. Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA). bibtex: '@inproceedings{Sharma_Demir_Ngonga Ngomo_Wehrheim, title={MLCHECK–Property-Driven Testing of Machine Learning Classifiers}, booktitle={Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, publisher={IEEE}, author={Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike} }' chicago: Sharma, Arnab, Caglar Demir, Axel-Cyrille Ngonga Ngomo, and Heike Wehrheim. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” In Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, n.d. ieee: A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” mla: Sharma, Arnab, et al. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE. short: 'A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, in: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, n.d.' date_created: 2021-12-07T11:11:36Z date_updated: 2022-01-06T06:58:02Z department: - _id: '7' - _id: '77' - _id: '574' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 - _id: '10' name: SFB 901 - Subproject B2 publication: Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA) publication_status: accepted publisher: IEEE status: public title: MLCHECK–Property-Driven Testing of Machine Learning Classifiers type: conference user_id: '477' year: '2021' ... --- _id: '21004' abstract: - lang: eng text: 'Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.' author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276' apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276' bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }' chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.' ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.' mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.' short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1. date_created: 2021-01-16T14:48:13Z date_updated: 2022-01-06T06:54:42Z department: - _id: '34' - _id: '355' - _id: '26' doi: 10.1109/tpami.2021.3051276 keyword: - Automated Machine Learning - Multi Label Classification - Hierarchical Planning - Bayesian Optimization language: - iso: eng page: 1-1 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_identifier: issn: - 0162-8828 - 2160-9292 - 1939-3539 publication_status: published status: public title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation' type: journal_article user_id: '5786' year: '2021' ... --- _id: '21092' abstract: - lang: eng text: "Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout.\r\nIn this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions." author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke} }' chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d. ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence. mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE. short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.). date_created: 2021-01-27T13:45:52Z date_updated: 2022-01-06T06:54:45Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_status: accepted publisher: IEEE status: public title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning type: journal_article user_id: '5786' year: '2021' ... --- _id: '21570' author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings of the Genetic and Evolutionary Computation Conference. ; 2021.' apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference. bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021} }' chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference, 2021. ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021. mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 2021. short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.' conference: end_date: 2021-07-14 name: Genetic and Evolutionary Computation Conference start_date: 2021-07-10 date_created: 2021-03-26T09:14:19Z date_updated: 2022-01-06T06:55:06Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proceedings of the Genetic and Evolutionary Computation Conference status: public title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance type: conference user_id: '5786' year: '2021' ... --- _id: '22913' author: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021.' apa: Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual). bibtex: '@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021} }' chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021. ieee: E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021. mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021. short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.' conference: end_date: 2021-09-17 location: Bilbao (Virtual) name: ECML/PKDD Workshop on Automating Data Science start_date: 2021-09-13 date_created: 2021-08-02T07:46:29Z date_updated: 2022-01-06T06:55:43Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 quality_controlled: '1' status: public title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning type: conference user_id: '5786' year: '2021' ... --- _id: '30866' abstract: - lang: eng text: "Automated machine learning (AutoML) strives for the automatic configuration\r\nof machine learning algorithms and their composition into an overall (software)\r\nsolution - a machine learning pipeline - tailored to the learning task\r\n(dataset) at hand. Over the last decade, AutoML has developed into an\r\nindependent research field with hundreds of contributions. While AutoML offers\r\nmany prospects, it is also known to be quite resource-intensive, which is one\r\nof its major points of criticism. The primary cause for a high resource\r\nconsumption is that many approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines while searching for good candidates. This problem is\r\namplified in the context of research on AutoML methods, due to large scale\r\nexperiments conducted with many datasets and approaches, each of them being run\r\nwith several repetitions to rule out random effects. In the spirit of recent\r\nwork on Green AI, this paper is written in an attempt to raise the awareness of\r\nAutoML researchers for the problem and to elaborate on possible remedies. To\r\nthis end, we identify four categories of actions the community may take towards\r\nmore sustainable research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking, transparency and research incentives." author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850. Published online 2021.' apa: 'Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions. In arXiv:2111.05850.' bibtex: '@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850}, author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }' chicago: 'Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.' ieee: 'T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” arXiv:2111.05850. 2021.' mla: 'Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.' short: T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier, ArXiv:2111.05850 (2021). date_created: 2022-04-12T11:57:15Z date_updated: 2022-04-12T12:01:23Z department: - _id: '34' - _id: '7' - _id: '26' external_id: arxiv: - '2111.05850' language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '10' name: 'SFB 901 - B2: SFB 901 - Subproject B2' publication: arXiv:2111.05850 status: public title: 'Towards Green Automated Machine Learning: Status Quo and Future Directions' type: preprint user_id: '38209' year: '2021' ... --- _id: '21198' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published online 2021.' apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.' bibtex: '@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }' chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” PAKDD, 2021.' ieee: 'J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” 2021.' mla: 'Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. 2021.' short: J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021). conference: end_date: 2021-05-14 location: Delhi, India name: The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021) start_date: 2021-05-11 date_created: 2021-02-09T09:30:14Z date_updated: 2022-08-24T12:49:06Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing series_title: PAKDD status: public title: 'Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data' type: conference user_id: '38209' year: '2021' ... --- _id: '17407' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.' apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020. bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Extreme Algorithm Selection with Dyadic Feature Representation}, booktitle={Discovery Science}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }' chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In Discovery Science, 2020. ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection with Dyadic Feature Representation,” presented at the Discovery Science 2020, 2020. mla: Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020. short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.' conference: name: Discovery Science 2020 date_created: 2020-07-21T10:06:51Z date_updated: 2022-01-06T06:53:10Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Discovery Science status: public title: Extreme Algorithm Selection with Dyadic Feature Representation type: conference user_id: '5786' year: '2020' ... --- _id: '17408' author: - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence. ; 2020.' apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.' bibtex: '@inproceedings{Hanselle_Tornede_Wever_Hüllermeier_2020, title={Hybrid Ranking and Regression for Algorithm Selection}, booktitle={KI 2020: Advances in Artificial Intelligence}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }' chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI 2020: Advances in Artificial Intelligence, 2020.' ieee: J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking and Regression for Algorithm Selection,” presented at the 43rd German Conference on Artificial Intelligence, 2020. mla: 'Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.' short: 'J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 2020.' conference: name: 43rd German Conference on Artificial Intelligence date_created: 2020-07-21T10:21:09Z date_updated: 2022-01-06T06:53:10Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: 'KI 2020: Advances in Artificial Intelligence' status: public title: Hybrid Ranking and Regression for Algorithm Selection type: conference user_id: '5786' year: '2020' ... --- _id: '17424' author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020. ; 2020. doi:10.1007/978-3-030-66770-2_8' apa: 'Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8' bibtex: '@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }' chicago: 'Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.' ieee: 'T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.' mla: 'Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.' short: 'T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.' conference: name: IOTStream Workshop @ ECMLPKDD 2020 date_created: 2020-07-28T09:17:41Z date_updated: 2022-01-06T06:53:11Z department: - _id: '34' - _id: '355' - _id: '26' doi: 10.1007/978-3-030-66770-2_8 language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '1' name: SFB 901 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proceedings of the ECMLPKDD 2020 status: public title: 'AutoML for Predictive Maintenance: One Tool to RUL Them All' type: conference user_id: '5786' year: '2020' ... --- _id: '20306' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.' apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online. bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }' chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020. ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020. mla: Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020. short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.' conference: location: Online name: Workshop MetaLearn 2020 @ NeurIPS 2020 date_created: 2020-11-06T09:42:27Z date_updated: 2022-01-06T06:54:26Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Workshop MetaLearn 2020 @ NeurIPS 2020 status: public title: Towards Meta-Algorithm Selection type: conference user_id: '5786' year: '2020' ... --- _id: '18276' abstract: - lang: eng text: "Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom a fixed set of candidate algorithms most suitable for a specific instance\r\nof an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training data for algorithm selection models is usually generated\r\nunder time constraints in the sense that not all algorithms are run to\r\ncompletion on all instances. Thus, training data usually comprises censored\r\ninformation, as the true runtime of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches are not able to handle such information in\r\na proper way. On the other side, survival analysis (SA) naturally supports\r\ncensored data and offers appropriate ways to use such data for learning\r\ndistributional models of algorithm runtime, as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive. Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse approach to algorithm\r\nselection, in which the avoidance of a timeout is given high priority. In an\r\nextensive experimental study with the standard benchmark ASlib, our approach is\r\nshown to be highly competitive and in many cases even superior to\r\nstate-of-the-art AS approaches." author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.' apa: 'Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.' bibtex: '@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}, booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }' chicago: 'Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In ACML 2020, 2020.' ieee: 'A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.' mla: 'Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.' short: 'A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.' conference: end_date: 2020-11-20 location: Bangkok, Thailand name: 12th Asian Conference on Machine Learning start_date: 2020-11-18 date_created: 2020-08-25T12:09:28Z date_updated: 2022-01-06T06:53:28Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - url: https://arxiv.org/pdf/2007.02816.pdf project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: ACML 2020 status: public title: 'Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis' type: conference user_id: '5786' year: '2020' ... --- _id: '15629' abstract: - lang: eng text: In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.' apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.' bibtex: '@inproceedings{Wever_Tornede_Mohr_Hüllermeier, title={LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}, publisher={Springer}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke} }' chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.” Springer, n.d.' ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,” presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.' mla: 'Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Springer.' short: 'M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.' conference: end_date: 2020-04-27 location: Konstanz, Germany name: Symposium on Intelligent Data Analysis start_date: 2020-04-24 date_created: 2020-01-23T08:44:08Z date_updated: 2022-01-06T06:52:30Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication_status: accepted publisher: Springer status: public title: 'LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification' type: conference user_id: '5786' year: '2020' ... --- _id: '15025' abstract: - lang: eng text: In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Lorijn full_name: van Rooijen, Lorijn id: '58843' last_name: van Rooijen - first_name: Heiko full_name: Hamann, Heiko last_name: Hamann citation: ama: Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266 apa: Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266 bibtex: '@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}, volume={28}, DOI={10.1162/evco_a_00266}, number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals}, author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020}, pages={165–193} }' chicago: 'Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266.' ieee: 'M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,” Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.' mla: Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266. short: M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193. date_created: 2019-11-18T14:19:19Z date_updated: 2022-01-06T06:52:15Z department: - _id: '34' - _id: '355' - _id: '26' - _id: '63' - _id: '238' doi: 10.1162/evco_a_00266 intvolume: ' 28' issue: '2' language: - iso: eng page: 165–193 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '9' name: SFB 901 - Subproject B1 - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Evolutionary Computation publication_status: published publisher: MIT Press Journals related_material: link: - relation: confirmation url: https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266 status: public title: Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets type: journal_article user_id: '15415' volume: 28 year: '2020' ... --- _id: '13770' author: - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Dennis full_name: Kundisch, Dennis id: '21117' last_name: Kundisch - first_name: Friedhelm full_name: Meyer auf der Heide, Friedhelm id: '15523' last_name: Meyer auf der Heide - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Karl H, Kundisch D, Meyer auf der Heide F, Wehrheim H. A Case for a New IT Ecosystem: On-The-Fly Computing. Business & Information Systems Engineering. 2020;62(6):467-481. doi:10.1007/s12599-019-00627-x' apa: 'Karl, H., Kundisch, D., Meyer auf der Heide, F., & Wehrheim, H. (2020). A Case for a New IT Ecosystem: On-The-Fly Computing. Business & Information Systems Engineering, 62(6), 467–481. https://doi.org/10.1007/s12599-019-00627-x' bibtex: '@article{Karl_Kundisch_Meyer auf der Heide_Wehrheim_2020, title={A Case for a New IT Ecosystem: On-The-Fly Computing}, volume={62}, DOI={10.1007/s12599-019-00627-x}, number={6}, journal={Business & Information Systems Engineering}, publisher={Springer}, author={Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and Wehrheim, Heike}, year={2020}, pages={467–481} }' chicago: 'Karl, Holger, Dennis Kundisch, Friedhelm Meyer auf der Heide, and Heike Wehrheim. “A Case for a New IT Ecosystem: On-The-Fly Computing.” Business & Information Systems Engineering 62, no. 6 (2020): 467–81. https://doi.org/10.1007/s12599-019-00627-x.' ieee: 'H. Karl, D. Kundisch, F. Meyer auf der Heide, and H. Wehrheim, “A Case for a New IT Ecosystem: On-The-Fly Computing,” Business & Information Systems Engineering, vol. 62, no. 6, pp. 467–481, 2020, doi: 10.1007/s12599-019-00627-x.' mla: 'Karl, Holger, et al. “A Case for a New IT Ecosystem: On-The-Fly Computing.” Business & Information Systems Engineering, vol. 62, no. 6, Springer, 2020, pp. 467–81, doi:10.1007/s12599-019-00627-x.' short: H. Karl, D. Kundisch, F. Meyer auf der Heide, H. Wehrheim, Business & Information Systems Engineering 62 (2020) 467–481. date_created: 2019-10-10T13:41:06Z date_updated: 2022-12-02T09:27:17Z ddc: - '004' department: - _id: '276' - _id: '75' - _id: '63' - _id: '77' doi: 10.1007/s12599-019-00627-x file: - access_level: closed content_type: application/pdf creator: ups date_created: 2019-12-12T10:24:47Z date_updated: 2019-12-12T10:24:47Z file_id: '15311' file_name: Karl2019_Article_ACaseForANewITEcosystemOn-The-.pdf file_size: 454532 relation: main_file success: 1 file_date_updated: 2019-12-12T10:24:47Z has_accepted_license: '1' intvolume: ' 62' issue: '6' language: - iso: eng page: 467-481 project: - _id: '1' name: SFB 901 - _id: '2' name: SFB 901 - Project Area A - _id: '3' name: SFB 901 - Project Area B - _id: '4' name: SFB 901 - Project Area C - _id: '82' name: SFB 901 - Project Area T - _id: '5' name: SFB 901 - Subproject A1 - _id: '6' name: SFB 901 - Subproject A2 - _id: '7' name: SFB 901 - Subproject A3 - _id: '8' name: SFB 901 - Subproject A4 - _id: '9' name: SFB 901 - Subproject B1 - _id: '10' name: SFB 901 - Subproject B2 - _id: '11' name: SFB 901 - Subproject B3 - _id: '12' name: SFB 901 - Subproject B4 - _id: '13' name: SFB 901 - Subproject C1 - _id: '14' name: SFB 901 - Subproject C2 - _id: '15' name: SFB 901 - Subproject C3 - _id: '16' name: SFB 901 - Subproject C4 - _id: '17' name: SFB 901 - Subproject C5 - _id: '83' name: SFB 901 -Subproject T1 - _id: '84' name: SFB 901 -Subproject T2 - _id: '107' name: SFB 901 -Subproject T3 - _id: '158' name: 'SFB 901 - T4: SFB 901 -Subproject T4' publication: Business & Information Systems Engineering publication_status: published publisher: Springer status: public title: 'A Case for a New IT Ecosystem: On-The-Fly Computing' type: journal_article user_id: '477' volume: 62 year: '2020' ... --- _id: '8868' author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Alexander full_name: Hetzer, Alexander id: '38209' last_name: Hetzer citation: ama: 'Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.' apa: Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany. bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }' chicago: Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019. ieee: M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine Learning for Multi-Label Classification,” presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany, 2019. mla: Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label Classification. 2019. short: 'M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.' conference: end_date: 2019-03-20 location: Bayreuth, Germany name: European Conference on Data Analytics (ECDA) start_date: 2019-03-18 date_created: 2019-04-10T07:17:55Z date_updated: 2022-01-06T07:04:04Z ddc: - '000' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: wever date_created: 2019-04-10T07:17:17Z date_updated: 2019-04-10T07:17:17Z file_id: '8870' file_name: Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf file_size: '74484' relation: main_file success: 1 file_date_updated: 2019-04-10T07:17:17Z has_accepted_license: '1' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing status: public title: Towards Automated Machine Learning for Multi-Label Classification type: conference_abstract user_id: '49109' year: '2019' ... --- _id: '15007' author: - first_name: Vitaly full_name: Melnikov, Vitaly id: '58747' last_name: Melnikov - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). ; 2019. doi:10.1016/j.jmva.2019.02.017' apa: 'Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101). https://doi.org/10.1016/j.jmva.2019.02.017' bibtex: '@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={10.1016/j.jmva.2019.02.017}, booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, year={2019} }' chicago: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.” In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019. https://doi.org/10.1016/j.jmva.2019.02.017.' ieee: 'V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.' mla: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.” Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019, doi:10.1016/j.jmva.2019.02.017.' short: 'V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.' date_created: 2019-11-15T10:43:26Z date_updated: 2022-01-06T06:52:14Z ddc: - '000' department: - _id: '34' - _id: '355' - _id: '7' doi: 10.1016/j.jmva.2019.02.017 file: - access_level: open_access content_type: application/pdf creator: lettmann date_created: 2020-02-28T12:47:07Z date_updated: 2020-02-28T12:47:07Z file_id: '16156' file_name: learning-to-aggregate-owa.pdf file_size: 2331320 relation: main_file file_date_updated: 2020-02-28T12:47:07Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B - _id: '1' name: SFB 901 publication: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101) publication_status: published status: public title: 'Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA' type: conference user_id: '477' year: '2019' ... --- _id: '15011' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.' apa: 'Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.' bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2019, title={Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking}, booktitle={Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019}, publisher={KIT Scientific Publishing, Karlsruhe}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Hoffmann, Frank and Hüllermeier, Eyke and Mikut, RalfEditors}, year={2019}, pages={135–146} }' chicago: 'Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, 135–46. KIT Scientific Publishing, Karlsruhe, 2019.' ieee: 'A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019, pp. 135–146.' mla: 'Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–46.' short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.' conference: end_date: 2019-11-29 location: Dortmund name: 29. Workshop Computational Intelligence start_date: 2019-11-28 date_created: 2019-11-15T13:29:25Z date_updated: 2022-01-06T06:52:14Z ddc: - '006' department: - _id: '355' editor: - first_name: Frank full_name: Hoffmann, Frank last_name: Hoffmann - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier - first_name: Ralf full_name: Mikut, Ralf last_name: Mikut file: - access_level: open_access content_type: application/pdf creator: ahetzer date_created: 2020-05-25T08:01:31Z date_updated: 2020-05-25T08:01:31Z file_id: '17060' file_name: ci_workshop_tornede.pdf file_size: 468825 relation: main_file file_date_updated: 2020-05-25T08:01:31Z has_accepted_license: '1' language: - iso: eng oa: '1' page: 135-146 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 publication_identifier: isbn: - 978-3-7315-0979-0 publication_status: published publisher: KIT Scientific Publishing, Karlsruhe status: public title: 'Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking' type: conference user_id: '38209' year: '2019' ... --- _id: '13132' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.' apa: 'Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.' bibtex: '@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.' ieee: 'F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.' mla: 'Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., 2019, pp. 273–74.' short: 'F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274.' conference: end_date: 2019-09-26 location: Kassel name: Informatik 2019 start_date: 2019-09-23 date_created: 2019-09-04T08:44:46Z date_updated: 2022-01-06T06:51:28Z department: - _id: '355' language: - iso: eng page: ' 273-274 ' place: Bonn project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: 'INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft' publisher: Gesellschaft für Informatik e.V. series_title: INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik status: public title: From Automated to On-The-Fly Machine Learning type: conference_abstract user_id: '38209' year: '2019' ... --- _id: '10232' abstract: - lang: eng text: Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.' apa: Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA. bibtex: '@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }' chicago: Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019. ieee: M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019. mla: Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019. short: 'M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.' conference: end_date: 2019-06-15 location: Long Beach, CA, USA name: 6th ICML Workshop on Automated Machine Learning (AutoML 2019) start_date: 2019-06-09 date_created: 2019-06-11T21:33:06Z date_updated: 2022-01-06T06:50:33Z ddc: - '006' department: - _id: '355' file: - access_level: open_access content_type: application/pdf creator: wever date_created: 2019-09-10T08:19:01Z date_updated: 2019-09-10T08:20:44Z file_id: '13177' file_name: Automating_MultiLabel_Classification_Extending_ML-Plan.pdf file_size: 388191 relation: main_file file_date_updated: 2019-09-10T08:20:44Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing status: public title: Automating Multi-Label Classification Extending ML-Plan type: conference user_id: '33176' year: '2019' ... --- _id: '2479' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Amin full_name: Faez, Amin last_name: Faez citation: ama: 'Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039' apa: 'Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039' bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco, CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services}, DOI={10.1109/SCC.2018.00039}, booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke and Faez, Amin}, year={2018} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.” In SCC. San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.' ieee: F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018. mla: Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039. short: 'F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018.' conference: end_date: 2018-07-07 location: San Francisco, CA, USA name: IEEE International Conference on Services Computing, SCC 2018 start_date: 2018-07-02 date_created: 2018-04-24T08:34:52Z date_updated: 2022-01-06T06:56:35Z ddc: - '000' department: - _id: '355' doi: 10.1109/SCC.2018.00039 file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:08:39Z date_updated: 2018-11-06T15:08:39Z file_id: '5382' file_name: 08456425.pdf file_size: 237890 relation: main_file file_date_updated: 2018-11-06T15:08:39Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://ieeexplore.ieee.org/document/8456425 oa: '1' place: San Francisco, CA, USA project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: SCC publication_status: published publisher: IEEE status: public title: (WIP) Towards the Automated Composition of Machine Learning Services type: conference user_id: '49109' year: '2018' ... --- _id: '2857' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Theodor full_name: Lettmann, Theodor id: '315' last_name: Lettmann orcid: 0000-0001-5859-2457 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.' apa: 'Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.' bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }' chicago: Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever. “Programmatic Task Network Planning.” In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, 31–39. AAAI, 2018. ieee: F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, Delft, Netherlands, 2018, pp. 31–39. mla: Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39. short: 'F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.' conference: end_date: 2018-06-29 location: Delft, Netherlands name: 28th International Conference on Automated Planning and Scheduling start_date: 2018-06-24 date_created: 2018-05-24T09:00:20Z date_updated: 2022-01-06T06:58:08Z ddc: - '000' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:18:26Z date_updated: 2018-11-06T15:18:26Z file_id: '5384' file_name: Mohr18ProgrammaticPlanning.pdf file_size: 349958 relation: main_file success: 1 file_date_updated: 2018-11-06T15:18:26Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf oa: '1' page: 31-39 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning publisher: AAAI status: public title: Programmatic Task Network Planning type: conference user_id: '315' year: '2018' ... --- _id: '2471' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036' apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036' bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA}, title={On-The-Fly Service Construction with Prototypes}, DOI={10.1109/SCC.2018.00036}, booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service Construction with Prototypes.” In SCC. San Francisco, CA, USA: IEEE Computer Society, 2018. https://doi.org/10.1109/SCC.2018.00036.' ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018. mla: Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC, IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036. short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.' conference: end_date: 2018-07-07 location: San Francisco, CA, USA name: IEEE International Conference on Services Computing, SCC 2018 start_date: 2018-07-02 date_created: 2018-04-23T11:40:20Z date_updated: 2022-01-06T06:56:32Z ddc: - '000' department: - _id: '355' doi: 10.1109/SCC.2018.00036 file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:15:38Z date_updated: 2018-11-06T15:15:38Z file_id: '5383' file_name: 08456422.pdf file_size: 356132 relation: main_file success: 1 file_date_updated: 2018-11-06T15:15:38Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://ieeexplore.ieee.org/abstract/document/8456422 oa: '1' place: San Francisco, CA, USA project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: SCC publisher: IEEE Computer Society status: public title: On-The-Fly Service Construction with Prototypes type: conference user_id: '49109' year: '2018' ... --- _id: '3510' abstract: - lang: eng text: Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches. article_type: original author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z' apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z' bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }' chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z.' ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi: 10.1007/s10994-018-5735-z.' mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.' short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515. conference: end_date: 2018-09-14 location: Dublin, Ireland name: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases start_date: 2018-09-10 date_created: 2018-07-08T14:06:14Z date_updated: 2022-01-06T06:59:21Z ddc: - '000' department: - _id: '355' - _id: '34' - _id: '7' - _id: '26' doi: 10.1007/s10994-018-5735-z file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T15:32:16Z date_updated: 2018-11-02T15:32:16Z file_id: '5306' file_name: ML-PlanAutomatedMachineLearnin.pdf file_size: 1070937 relation: main_file success: 1 file_date_updated: 2018-11-02T15:32:16Z has_accepted_license: '1' keyword: - AutoML - Hierarchical Planning - HTN planning - ML-Plan language: - iso: eng main_file_link: - open_access: '1' url: https://rdcu.be/3Nc2 oa: '1' page: 1495-1515 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Machine Learning publication_identifier: eissn: - 1573-0565 issn: - 0885-6125 publication_status: epub_ahead publisher: Springer status: public title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning' type: journal_article user_id: '5786' year: '2018' ... --- _id: '3552' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. doi:10.1007/978-3-030-01768-2_19' apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19 bibtex: '@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands}, title={Reduction Stumps for Multi-Class Classification}, DOI={10.1007/978-3-030-01768-2_19}, booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }' chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps for Multi-Class Classification.” In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands, n.d. https://doi.org/10.1007/978-3-030-01768-2_19. ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class Classification,” in Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands. mla: Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” Proceedings of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19. short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.' conference: end_date: 2018-10-26 location: ‘s-Hertogenbosch, the Netherlands name: Symposium on Intelligent Data Analysis start_date: 2018-10-24 date_created: 2018-07-13T15:29:15Z date_updated: 2022-01-06T06:59:25Z ddc: - '000' department: - _id: '355' doi: 10.1007/978-3-030-01768-2_19 file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:23:02Z date_updated: 2018-11-06T15:23:02Z file_id: '5385' file_name: Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf file_size: 1348768 relation: main_file success: 1 file_date_updated: 2018-11-06T15:23:02Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19 oa: '1' place: ‘s-Hertogenbosch, the Netherlands project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the Symposium on Intelligent Data Analysis publication_status: accepted quality_controlled: '1' status: public title: Reduction Stumps for Multi-Class Classification type: conference user_id: '49109' year: '2018' ... --- _id: '3852' abstract: - lang: eng text: "In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.\r\nVarious AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.\r\nMore specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.\r\nIn this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.\r\nWe evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT." author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.' apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden. bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }' chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018. ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018. mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” ICML 2018 AutoML Workshop, 2018. short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.' conference: end_date: 2018-07-15 location: Stockholm, Sweden name: ICML 2018 AutoML Workshop start_date: 2018-07-10 date_created: 2018-08-09T06:14:54Z date_updated: 2022-01-06T06:59:46Z ddc: - '006' department: - _id: '355' file: - access_level: open_access content_type: application/pdf creator: wever date_created: 2018-08-09T06:14:43Z date_updated: 2018-08-09T06:14:43Z file_id: '3853' file_name: 38.pdf file_size: 297811 relation: main_file file_date_updated: 2018-08-09T06:14:43Z has_accepted_license: '1' keyword: - automated machine learning - complex pipelines - hierarchical planning language: - iso: eng main_file_link: - url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx oa: '1' project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: ICML 2018 AutoML Workshop quality_controlled: '1' status: public title: ML-Plan for Unlimited-Length Machine Learning Pipelines type: conference urn: '38527' user_id: '49109' year: '2018' ... --- _id: '2109' abstract: - lang: eng text: In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for Classification. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM; 2018. doi:10.1145/3205455.3205562' apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM. https://doi.org/10.1145/3205455.3205562' bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles of Evolved Nested Dichotomies for Classification}, DOI={10.1145/3205455.3205562}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }' chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.” In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM, 2018. https://doi.org/10.1145/3205455.3205562.' ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018. mla: Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classification.” Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 2018, doi:10.1145/3205455.3205562. short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.' conference: end_date: 2018-07-19 location: Kyoto, Japan name: GECCO 2018 start_date: 2018-07-15 date_created: 2018-03-31T13:51:23Z date_updated: 2022-01-06T06:54:45Z ddc: - '000' department: - _id: '355' doi: 10.1145/3205455.3205562 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T14:33:54Z date_updated: 2018-11-02T14:33:54Z file_id: '5275' file_name: p561-wever.pdf file_size: 875404 relation: main_file success: 1 file_date_updated: 2018-11-02T14:33:54Z has_accepted_license: '1' keyword: - Classification - Hierarchical Decomposition - Indirect Encoding language: - iso: eng main_file_link: - open_access: '1' url: https://dl.acm.org/citation.cfm?doid=3205455.3205562 oa: '1' place: Kyoto, Japan project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018 publication_status: published publisher: ACM status: public title: Ensembles of Evolved Nested Dichotomies for Classification type: conference user_id: '33176' year: '2018' ... --- _id: '17713' author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. Published online 2018. apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv. bibtex: '@article{Wever_Mohr_Hüllermeier_2018, title={Automated Multi-Label Classification based on ML-Plan}, publisher={Arxiv}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }' chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018. ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018. mla: Wever, Marcel Dominik, et al. Automated Multi-Label Classification Based on ML-Plan. Arxiv, 2018. short: M.D. Wever, F. Mohr, E. Hüllermeier, (2018). date_created: 2020-08-07T11:38:10Z date_updated: 2022-01-06T06:53:17Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/1811.04060.pdf oa: '1' project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publisher: Arxiv status: public title: Automated Multi-Label Classification based on ML-Plan type: preprint user_id: '5786' year: '2018' ... --- _id: '17714' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition. Published online 2018. apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine learning service composition. bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={Automated machine learning service composition}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }' chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine Learning Service Composition,” 2018. ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018. mla: Mohr, Felix, et al. Automated Machine Learning Service Composition. 2018. short: F. Mohr, M.D. Wever, E. Hüllermeier, (2018). date_created: 2020-08-07T11:40:13Z date_updated: 2022-01-06T06:53:17Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/1809.00486.pdf oa: '1' project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing status: public title: Automated machine learning service composition type: preprint user_id: '5786' year: '2018' ... --- _id: '6423' author: - first_name: Dirk full_name: Schäfer, Dirk last_name: Schäfer - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Discovery Science. Cham: Springer International Publishing; 2018:161-175. doi:10.1007/978-3-030-01771-2_11' apa: 'Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11' bibtex: '@inbook{Schäfer_Hüllermeier_2018, place={Cham}, title={Preference-Based Reinforcement Learning Using Dyad Ranking}, DOI={10.1007/978-3-030-01771-2_11}, booktitle={Discovery Science}, publisher={Springer International Publishing}, author={Schäfer, Dirk and Hüllermeier, Eyke}, year={2018}, pages={161–175} }' chicago: 'Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In Discovery Science, 161–75. Cham: Springer International Publishing, 2018. https://doi.org/10.1007/978-3-030-01771-2_11.' ieee: 'D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.' mla: Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” Discovery Science, Springer International Publishing, 2018, pp. 161–75, doi:10.1007/978-3-030-01771-2_11. short: 'D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International Publishing, Cham, 2018, pp. 161–175.' date_created: 2018-12-20T15:52:03Z date_updated: 2022-01-06T07:03:04Z ddc: - '000' department: - _id: '355' doi: 10.1007/978-3-030-01771-2_11 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2019-01-11T11:03:50Z date_updated: 2019-01-11T11:03:50Z file_id: '6623' file_name: Schäfer-Hüllermeier2018_Chapter_Preference-BasedReinforcementL.pdf file_size: 458972 relation: main_file success: 1 file_date_updated: 2019-01-11T11:03:50Z has_accepted_license: '1' language: - iso: eng page: 161-175 place: Cham project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: Discovery Science publication_identifier: isbn: - '9783030017705' - '9783030017712' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer International Publishing status: public title: Preference-Based Reinforcement Learning Using Dyad Ranking type: book_chapter user_id: '49109' year: '2018' ... --- _id: '115' abstract: - lang: eng text: 'Whenever customers have to decide between different instances of the same product, they are interested in buying the best product. In contrast, companies are interested in reducing the construction effort (and usually as a consequence thereof, the quality) to gain profit. The described setting is widely known as opposed preferences in quality of the product and also applies to the context of service-oriented computing. In general, service-oriented computing emphasizes the construction of large software systems out of existing services, where services are small and self-contained pieces of software that adhere to a specified interface. Several implementations of the same interface are considered as several instances of the same service. Thereby, customers are interested in buying the best service implementation for their service composition wrt. to metrics, such as costs, energy, memory consumption, or execution time. One way to ensure the service quality is to employ certificates, which can come in different kinds: Technical certificates proving correctness can be automatically constructed by the service provider and again be automatically checked by the user. Digital certificates allow proof of the integrity of a product. Other certificates might be rolled out if service providers follow a good software construction principle, which is checked in annual audits. Whereas all of these certificates are handled differently in service markets, what they have in common is that they influence the buying decisions of customers. In this paper, we review state-of-the-art developments in certification with respect to service-oriented computing. We not only discuss how certificates are constructed and handled in service-oriented computing but also review the effects of certificates on the market from an economic perspective.' author: - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Julia full_name: Krämer, Julia last_name: Krämer - first_name: Dirk full_name: van Straaten, Dirk id: '10311' last_name: van Straaten - first_name: Theodor full_name: Lettmann, Theodor id: '315' last_name: Lettmann orcid: 0000-0001-5859-2457 citation: ama: 'Jakobs M-C, Krämer J, van Straaten D, Lettmann T. Certification Matters for Service Markets. In: Marcelo De Barros, Janusz Klink,Tadeus Uhl TP, ed. The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION). ; 2017:7-12.' apa: Jakobs, M.-C., Krämer, J., van Straaten, D., & Lettmann, T. (2017). Certification Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 7–12). bibtex: '@inproceedings{Jakobs_Krämer_van Straaten_Lettmann_2017, title={Certification Matters for Service Markets}, booktitle={The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}, author={Jakobs, Marie-Christine and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}, editor={Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas PrinzEditor}, year={2017}, pages={7–12} }' chicago: Jakobs, Marie-Christine, Julia Krämer, Dirk van Straaten, and Theodor Lettmann. “Certification Matters for Service Markets.” In The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus Uhl, 7–12, 2017. ieee: M.-C. Jakobs, J. Krämer, D. van Straaten, and T. Lettmann, “Certification Matters for Service Markets,” in The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12. mla: Jakobs, Marie-Christine, et al. “Certification Matters for Service Markets.” The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus Uhl, 2017, pp. 7–12. short: 'M.-C. Jakobs, J. Krämer, D. van Straaten, T. Lettmann, in: T.P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.' date_created: 2017-10-17T12:41:14Z date_updated: 2022-01-06T06:51:02Z ddc: - '040' department: - _id: '77' - _id: '355' - _id: '179' editor: - first_name: Thomas Prinz full_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz last_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T13:04:12Z date_updated: 2018-03-21T13:04:12Z file_id: '1564' file_name: 115-JakobsKraemerVanStraatenLettmann2017.pdf file_size: 133531 relation: main_file success: 1 file_date_updated: 2018-03-21T13:04:12Z has_accepted_license: '1' language: - iso: eng page: 7-12 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '11' name: SFB 901 - Subproject B3 - _id: '12' name: SFB 901 - Subproject B4 - _id: '8' name: SFB 901 - Subproject A4 - _id: '2' name: SFB 901 - Project Area A - _id: '3' name: SFB 901 - Project Area B publication: The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) status: public title: Certification Matters for Service Markets type: conference user_id: '477' year: '2017' ... --- _id: '71' abstract: - lang: eng text: Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6). author: - first_name: Mike full_name: Czech, Mike last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Tools. In: Proceedings of the 3rd International Workshop on Software Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262' apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262 bibtex: '@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, series={SWAN’17}, title={Predicting Rankings of Software Verification Tools}, DOI={10.1145/3121257.3121262}, booktitle={Proceedings of the 3rd International Workshop on Software Analytics}, author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017}, pages={23–26}, collection={SWAN’17} }' chicago: Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In Proceedings of the 3rd International Workshop on Software Analytics, 23–26. SWAN’17, 2017. https://doi.org/10.1145/3121257.3121262. ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings of Software Verification Tools,” in Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26. mla: Czech, Mike, et al. “Predicting Rankings of Software Verification Tools.” Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26, doi:10.1145/3121257.3121262. short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.' date_created: 2017-10-17T12:41:05Z date_updated: 2022-01-06T07:03:28Z ddc: - '000' department: - _id: '355' - _id: '77' doi: 10.1145/3121257.3121262 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T14:24:29Z date_updated: 2018-11-02T14:24:29Z file_id: '5271' file_name: fsews17swan-swanmain1.pdf file_size: 822383 relation: main_file success: 1 file_date_updated: 2018-11-02T14:24:29Z has_accepted_license: '1' language: - iso: eng page: 23-26 project: - _id: '1' name: SFB 901 - _id: '12' name: SFB 901 - Subprojekt B4 - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 publication: Proceedings of the 3rd International Workshop on Software Analytics series_title: SWAN'17 status: public title: Predicting Rankings of Software Verification Tools type: conference user_id: '15504' year: '2017' ... --- _id: '1180' abstract: - lang: eng text: These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence. Dortmund; 2017.' apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.' bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2017} }' chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop Computational Intelligence. Dortmund, 2017.' ieee: 'M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.' mla: 'Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence, 2017.' short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.' conference: end_date: 2017-11-24 location: Dortmund name: 27th Workshop Computational Intelligence start_date: 2017-11-23 date_created: 2018-02-22T07:19:18Z date_updated: 2022-01-06T06:51:09Z ddc: - '000' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:28:09Z date_updated: 2018-11-06T15:28:09Z file_id: '5387' file_name: CI Workshop AutoML.pdf file_size: 323589 relation: main_file success: 1 file_date_updated: 2018-11-06T15:28:09Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://publikationen.bibliothek.kit.edu/1000074341/4643874 oa: '1' place: Dortmund project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: 27th Workshop Computational Intelligence publication_status: published status: public title: 'Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization' type: conference user_id: '49109' year: '2017' ... --- _id: '190' abstract: - lang: eng text: Today, software components are provided by global markets in the form of services. In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester’s requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user’s decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach. author: - first_name: Marie Christin full_name: Platenius, Marie Christin last_name: Platenius - first_name: Ammar full_name: Shaker, Ammar last_name: Shaker - first_name: Matthias full_name: Becker, Matthias last_name: Becker - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Wilhelm full_name: Schäfer, Wilhelm last_name: Schäfer citation: ama: Platenius MC, Shaker A, Becker M, Hüllermeier E, Schäfer W. Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017. 2016;(8):739-759. doi:10.1109/TSE.2016.2632115 apa: Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2016). Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, (8), 739–759. https://doi.org/10.1109/TSE.2016.2632115 bibtex: '@article{Platenius_Shaker_Becker_Hüllermeier_Schäfer_2016, title={Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic}, DOI={10.1109/TSE.2016.2632115}, number={8}, journal={IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017}, publisher={IEEE}, author={Platenius, Marie Christin and Shaker, Ammar and Becker, Matthias and Hüllermeier, Eyke and Schäfer, Wilhelm}, year={2016}, pages={739–759} }' chicago: 'Platenius, Marie Christin, Ammar Shaker, Matthias Becker, Eyke Hüllermeier, and Wilhelm Schäfer. “Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic.” IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, no. 8 (2016): 739–59. https://doi.org/10.1109/TSE.2016.2632115.' ieee: M. C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, and W. Schäfer, “Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic,” IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017, no. 8, pp. 739–759, 2016. mla: Platenius, Marie Christin, et al. “Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic.” IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, no. 8, IEEE, 2016, pp. 739–59, doi:10.1109/TSE.2016.2632115. short: M.C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, W. Schäfer, IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017 (2016) 739–759. date_created: 2017-10-17T12:41:29Z date_updated: 2022-01-06T06:53:57Z ddc: - '040' department: - _id: '355' doi: 10.1109/TSE.2016.2632115 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T12:30:31Z date_updated: 2018-03-21T12:30:31Z file_id: '1529' file_name: 190-07755807.pdf file_size: 5225413 relation: main_file success: 1 file_date_updated: 2018-03-21T12:30:31Z has_accepted_license: '1' issue: '8' language: - iso: eng page: 739-759 project: - _id: '1' name: SFB 901 - _id: '9' name: SFB 901 - Subprojekt B1 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '11' name: SFB 901 - Subprojekt B3 - _id: '3' name: SFB 901 - Project Area B publication: IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017 publisher: IEEE status: public title: Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic type: journal_article user_id: '15504' year: '2016' ... --- _id: '225' abstract: - lang: eng text: Image Processing is fundamental for any camera-based vision system. In order to automate the prototyping process of image processing solutions to some extend, we propose a holistic, adaptive approach that comprises concepts for specification, composition, recommendation, execution, and rating of image processing functionality. The fundamental idea is to realize image processing applications according to Service-oriented Computing design principles. That is, distinct image processing functionality is encapsulated in terms of stateless services. Services are then used as building blocks for more complex image processing functionality. To automatically compose complex image processing functionality, our proposed approach incorporates a flexible, Artificial Intelligence planning-based forward search algorithm. Decision-making between alternative composition steps is supported by a learning recommendation system, which keeps track of valid composition steps by automatically constructing a composition grammar. In addition, it adapts to solutions of high quality by means of feedback-based Reinforcement Learning techniques. A concrete use case serves as proof of concept and demonstrates the feasibility of our holistic, adaptive approach. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Kleinjohann B. A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality. In: Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). ; 2016:1--8. doi:10.1109/ETFA.2016.7733522' apa: Jungmann, A., & Kleinjohann, B. (2016). A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality. In Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1--8). https://doi.org/10.1109/ETFA.2016.7733522 bibtex: '@inproceedings{Jungmann_Kleinjohann_2016, title={A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality}, DOI={10.1109/ETFA.2016.7733522}, booktitle={Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}, author={Jungmann, Alexander and Kleinjohann, Bernd}, year={2016}, pages={1--8} }' chicago: Jungmann, Alexander, and Bernd Kleinjohann. “A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality.” In Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1--8, 2016. https://doi.org/10.1109/ETFA.2016.7733522. ieee: A. Jungmann and B. Kleinjohann, “A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality,” in Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2016, pp. 1--8. mla: Jungmann, Alexander, and Bernd Kleinjohann. “A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality.” Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2016, pp. 1--8, doi:10.1109/ETFA.2016.7733522. short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2016, pp. 1--8.' date_created: 2017-10-17T12:41:35Z date_updated: 2022-01-06T06:55:34Z ddc: - '040' doi: 10.1109/ETFA.2016.7733522 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T10:34:35Z date_updated: 2018-03-21T10:34:35Z file_id: '1508' file_name: 225-07733522.pdf file_size: 1323587 relation: main_file success: 1 file_date_updated: 2018-03-21T10:34:35Z has_accepted_license: '1' page: 1--8 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) status: public title: A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality type: conference user_id: '15504' year: '2016' ... --- _id: '218' abstract: - lang: eng text: In the Image Processing domain, automated generation of complex Image Processing functionality is highly desirable; e.g., for rapid prototyping. Service composition techniques, in turn, facilitate automated generation of complex functionality based on building blocks in terms of services. For that reason, we aim for transferring the Service Composition paradigm into the Image Processing domain. In this paper, we present our symbolic composition approach that enables us to automatically generate Image Processing applications. Functionality of Image Processing services is described by means of a variant of first-order logic, which grounds on domain knowledge operationalized in terms of ontologies. A Petri-net formalism serves as basis for modeling data-flow of services and composed services. A planning-based composition algorithm automatically composes complex data-flow for a required functionality. A brief evaluation serves as proof of concept. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Kleinjohann B. Automatic Composition of Service-based Image Processing Applications. In: Proceedings of the 13th IEEE International Conference on Services Computing (SCC). ; 2016:106--113. doi:10.1109/SCC.2016.21' apa: Jungmann, A., & Kleinjohann, B. (2016). Automatic Composition of Service-based Image Processing Applications. In Proceedings of the 13th IEEE International Conference on Services Computing (SCC) (pp. 106--113). https://doi.org/10.1109/SCC.2016.21 bibtex: '@inproceedings{Jungmann_Kleinjohann_2016, title={Automatic Composition of Service-based Image Processing Applications}, DOI={10.1109/SCC.2016.21}, booktitle={Proceedings of the 13th IEEE International Conference on Services Computing (SCC)}, author={Jungmann, Alexander and Kleinjohann, Bernd}, year={2016}, pages={106--113} }' chicago: Jungmann, Alexander, and Bernd Kleinjohann. “Automatic Composition of Service-Based Image Processing Applications.” In Proceedings of the 13th IEEE International Conference on Services Computing (SCC), 106--113, 2016. https://doi.org/10.1109/SCC.2016.21. ieee: A. Jungmann and B. Kleinjohann, “Automatic Composition of Service-based Image Processing Applications,” in Proceedings of the 13th IEEE International Conference on Services Computing (SCC), 2016, pp. 106--113. mla: Jungmann, Alexander, and Bernd Kleinjohann. “Automatic Composition of Service-Based Image Processing Applications.” Proceedings of the 13th IEEE International Conference on Services Computing (SCC), 2016, pp. 106--113, doi:10.1109/SCC.2016.21. short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 13th IEEE International Conference on Services Computing (SCC), 2016, pp. 106--113.' date_created: 2017-10-17T12:41:34Z date_updated: 2022-01-06T06:55:14Z ddc: - '040' doi: 10.1109/SCC.2016.21 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T10:38:44Z date_updated: 2018-03-21T10:38:44Z file_id: '1515' file_name: 218-07557442.pdf file_size: 836658 relation: main_file success: 1 file_date_updated: 2018-03-21T10:38:44Z has_accepted_license: '1' page: 106--113 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 13th IEEE International Conference on Services Computing (SCC) status: public title: Automatic Composition of Service-based Image Processing Applications type: conference user_id: '15504' year: '2016' ... --- _id: '280' abstract: - lang: eng text: The Collaborative Research Centre "On-The-Fly Computing" works on foundations and principles for the vision of the Future Internet. It proposes the paradigm of On-The-Fly Computing, which tackles emerging worldwide service markets. In these markets, service providers trade software, platform, and infrastructure as a service. Service requesters state requirements on services. To satisfy these requirements, the new role of brokers, who are (human) actors building service compositions on the fly, is introduced. Brokers have to specify service compositions formally and comprehensively using a domain-specific language (DSL), and to use service matching for the discovery of the constituent services available in the market. The broker's choice of the DSL and matching approaches influences her success of building compositions as distinctive properties of different service markets play a significant role. In this paper, we propose a new approach of engineering a situation-specific DSL by customizing a comprehensive, modular DSL and its matching for given service market properties. This enables the broker to create market-specific composition specifications and to perform market-specific service matching. As a result, the broker builds service compositions satisfying the requester's requirements more accurately. We evaluated the presented concepts using case studies in service markets for tourism and university management. author: - first_name: Svetlana full_name: Arifulina, Svetlana last_name: Arifulina - first_name: Marie Christin full_name: Platenius, Marie Christin last_name: Platenius - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Gregor full_name: Engels, Gregor id: '107' last_name: Engels - first_name: Wilhelm full_name: Schäfer, Wilhelm last_name: Schäfer citation: ama: 'Arifulina S, Platenius MC, Mohr F, Engels G, Schäfer W. Market-Specific Service Compositions: Specification and Matching. In: Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet. ; 2015:333--340. doi:10.1109/SERVICES.2015.58' apa: 'Arifulina, S., Platenius, M. C., Mohr, F., Engels, G., & Schäfer, W. (2015). Market-Specific Service Compositions: Specification and Matching. In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet (pp. 333--340). https://doi.org/10.1109/SERVICES.2015.58' bibtex: '@inproceedings{Arifulina_Platenius_Mohr_Engels_Schäfer_2015, title={Market-Specific Service Compositions: Specification and Matching}, DOI={10.1109/SERVICES.2015.58}, booktitle={Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet}, author={Arifulina, Svetlana and Platenius, Marie Christin and Mohr, Felix and Engels, Gregor and Schäfer, Wilhelm}, year={2015}, pages={333--340} }' chicago: 'Arifulina, Svetlana, Marie Christin Platenius, Felix Mohr, Gregor Engels, and Wilhelm Schäfer. “Market-Specific Service Compositions: Specification and Matching.” In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 333--340, 2015. https://doi.org/10.1109/SERVICES.2015.58.' ieee: 'S. Arifulina, M. C. Platenius, F. Mohr, G. Engels, and W. Schäfer, “Market-Specific Service Compositions: Specification and Matching,” in Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340.' mla: 'Arifulina, Svetlana, et al. “Market-Specific Service Compositions: Specification and Matching.” Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340, doi:10.1109/SERVICES.2015.58.' short: 'S. Arifulina, M.C. Platenius, F. Mohr, G. Engels, W. Schäfer, in: Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340.' date_created: 2017-10-17T12:41:46Z date_updated: 2022-01-06T06:57:49Z ddc: - '040' department: - _id: '66' - _id: '76' - _id: '355' doi: 10.1109/SERVICES.2015.58 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T09:26:04Z date_updated: 2018-03-21T09:26:04Z file_id: '1470' file_name: 280-07196546.pdf file_size: 234260 relation: main_file success: 1 file_date_updated: 2018-03-21T09:26:04Z has_accepted_license: '1' language: - iso: eng page: 333--340 project: - _id: '1' name: SFB 901 - _id: '9' name: SFB 901 - Subprojekt B1 - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B publication: 'Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet' status: public title: 'Market-Specific Service Compositions: Specification and Matching' type: conference user_id: '477' year: '2015' ... --- _id: '245' abstract: - lang: eng text: In cloud computing, software architects develop systems for virtually unlimited resources that cloud providers account on a pay-per-use basis. Elasticity management systems provision these resources autonomously to deal with changing workload. Such changing workloads call for new objective metrics allowing architects to quantify quality properties like scalability, elasticity, and efficiency, e.g., for requirements/SLO engineering and software design analysis. In literature, initial metrics for these properties have been proposed. However, current metrics lack a systematic derivation and assume knowledge of implementation details like resource handling. Therefore, these metrics are inapplicable where such knowledge is unavailable.To cope with these lacks, this short paper derives metrics for scalability, elasticity, and efficiency properties of cloud computing systems using the goal question metric (GQM) method. Our derivation uses a running example that outlines characteristics of cloud computing systems. Eventually, this example allows us to set up a systematic GQM plan and to derive an initial set of six new metrics. We particularly show that our GQM plan allows to classify existing metrics. author: - first_name: Matthias full_name: Becker, Matthias last_name: Becker - first_name: Sebastian full_name: Lehrig, Sebastian last_name: Lehrig - first_name: Steffen full_name: Becker, Steffen last_name: Becker citation: ama: 'Becker M, Lehrig S, Becker S. Systematically Deriving Quality Metrics for Cloud Computing Systems. In: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering. ICPE ’15. New York, NY, USA; 2015:169--174. doi:10.1145/2668930.2688043' apa: Becker, M., Lehrig, S., & Becker, S. (2015). Systematically Deriving Quality Metrics for Cloud Computing Systems. In Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering (pp. 169--174). New York, NY, USA. https://doi.org/10.1145/2668930.2688043 bibtex: '@inproceedings{Becker_Lehrig_Becker_2015, place={New York, NY, USA}, series={ICPE ’15}, title={Systematically Deriving Quality Metrics for Cloud Computing Systems}, DOI={10.1145/2668930.2688043}, booktitle={Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering}, author={Becker, Matthias and Lehrig, Sebastian and Becker, Steffen}, year={2015}, pages={169--174}, collection={ICPE ’15} }' chicago: Becker, Matthias, Sebastian Lehrig, and Steffen Becker. “Systematically Deriving Quality Metrics for Cloud Computing Systems.” In Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, 169--174. ICPE ’15. New York, NY, USA, 2015. https://doi.org/10.1145/2668930.2688043. ieee: M. Becker, S. Lehrig, and S. Becker, “Systematically Deriving Quality Metrics for Cloud Computing Systems,” in Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, 2015, pp. 169--174. mla: Becker, Matthias, et al. “Systematically Deriving Quality Metrics for Cloud Computing Systems.” Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, 2015, pp. 169--174, doi:10.1145/2668930.2688043. short: 'M. Becker, S. Lehrig, S. Becker, in: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, New York, NY, USA, 2015, pp. 169--174.' date_created: 2017-10-17T12:41:39Z date_updated: 2022-01-06T06:56:26Z ddc: - '040' doi: 10.1145/2668930.2688043 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T09:47:47Z date_updated: 2018-03-21T09:47:47Z file_id: '1493' file_name: 245-paper_02.pdf file_size: 462675 relation: main_file success: 1 file_date_updated: 2018-03-21T09:47:47Z has_accepted_license: '1' language: - iso: eng page: 169--174 place: New York, NY, USA project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering series_title: ICPE '15 status: public title: Systematically Deriving Quality Metrics for Cloud Computing Systems type: conference user_id: '477' year: '2015' ... --- _id: '323' abstract: - lang: eng text: On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Felix full_name: Mohr, Felix last_name: Mohr citation: ama: Jungmann A, Mohr F. An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications. 2015;(1):1-18. doi:10.1186/s13174-015-0022-8 apa: Jungmann, A., & Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications, (1), 1–18. https://doi.org/10.1186/s13174-015-0022-8 bibtex: '@article{Jungmann_Mohr_2015, title={An approach towards adaptive service composition in markets of composed services}, DOI={10.1186/s13174-015-0022-8}, number={1}, journal={Journal of Internet Services and Applications}, publisher={Springer}, author={Jungmann, Alexander and Mohr, Felix}, year={2015}, pages={1–18} }' chicago: 'Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” Journal of Internet Services and Applications, no. 1 (2015): 1–18. https://doi.org/10.1186/s13174-015-0022-8.' ieee: A. Jungmann and F. Mohr, “An approach towards adaptive service composition in markets of composed services,” Journal of Internet Services and Applications, no. 1, pp. 1–18, 2015. mla: Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” Journal of Internet Services and Applications, no. 1, Springer, 2015, pp. 1–18, doi:10.1186/s13174-015-0022-8. short: A. Jungmann, F. Mohr, Journal of Internet Services and Applications (2015) 1–18. date_created: 2017-10-17T12:41:55Z date_updated: 2022-01-06T06:59:06Z ddc: - '040' department: - _id: '355' doi: 10.1186/s13174-015-0022-8 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:39:17Z date_updated: 2018-03-20T07:39:17Z file_id: '1429' file_name: 323-An_approach_towards_adaptive_service_composition_in_markets_of_composed_services.pdf file_size: 2842281 relation: main_file success: 1 file_date_updated: 2018-03-20T07:39:17Z has_accepted_license: '1' issue: '1' language: - iso: eng page: 1-18 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Journal of Internet Services and Applications publisher: Springer status: public title: An approach towards adaptive service composition in markets of composed services type: journal_article user_id: '477' year: '2015' ... --- _id: '324' abstract: - lang: eng text: Services are self-contained software components that can beused platform independent and that aim at maximizing software reuse. Abasic concern in service oriented architectures is to measure the reusabilityof services. One of the most important qualities is the functionalreusability, which indicates how relevant the task is that a service solves.Current metrics for functional reusability of software, however, have verylittle explanatory power and do not accomplish this goal.This paper presents a new approach to estimate the functional reusabilityof services based on their relevance. To this end, it denes the degreeto which a service enables the execution of other services as its contri-bution. Based on the contribution, relevance of services is dened as anestimation for their functional reusability. Explanatory power is obtainedby normalizing relevance values with a reference service. The applicationof the metric to a service test set conrms its supposed capabilities. author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr citation: ama: 'Mohr F. A Metric for Functional Reusability of Services. In: Proceedings of the 14th International Conference on Software Reuse (ICSR). LNCS. ; 2015:298--313. doi:10.1007/978-3-319-14130-5_21' apa: Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313). https://doi.org/10.1007/978-3-319-14130-5_21 bibtex: '@inproceedings{Mohr_2015, series={LNCS}, title={A Metric for Functional Reusability of Services}, DOI={10.1007/978-3-319-14130-5_21}, booktitle={Proceedings of the 14th International Conference on Software Reuse (ICSR)}, author={Mohr, Felix}, year={2015}, pages={298--313}, collection={LNCS} }' chicago: Mohr, Felix. “A Metric for Functional Reusability of Services.” In Proceedings of the 14th International Conference on Software Reuse (ICSR), 298--313. LNCS, 2015. https://doi.org/10.1007/978-3-319-14130-5_21. ieee: F. Mohr, “A Metric for Functional Reusability of Services,” in Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313. mla: Mohr, Felix. “A Metric for Functional Reusability of Services.” Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313, doi:10.1007/978-3-319-14130-5_21. short: 'F. Mohr, in: Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313.' date_created: 2017-10-17T12:41:55Z date_updated: 2022-01-06T06:59:07Z ddc: - '040' department: - _id: '355' doi: 10.1007/978-3-319-14130-5_21 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:38:44Z date_updated: 2018-03-20T07:38:44Z file_id: '1428' file_name: 324-ICSR-Mohr-15.pdf file_size: 569475 relation: main_file success: 1 file_date_updated: 2018-03-20T07:38:44Z has_accepted_license: '1' language: - iso: eng page: 298--313 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 14th International Conference on Software Reuse (ICSR) series_title: LNCS status: public title: A Metric for Functional Reusability of Services type: conference user_id: '477' year: '2015' ... --- _id: '3343' abstract: - lang: eng text: In this paper we consider an extended variant of query learning where the hidden concept is embedded in some Boolean circuit. This additional processing layer modifies query arguments and answers by fixed transformation functions which are known to the learner. For this scenario, we provide a characterization of the solution space and an ordering on it. We give a compact representation of the minimal and maximal solutions as quantified Boolean formulas and we adapt the original algorithms for exact learning of specific classes of propositional formulas. author: - first_name: Uwe full_name: Bubeck, Uwe last_name: Bubeck - first_name: Hans full_name: Kleine Büning, Hans last_name: Kleine Büning citation: ama: Bubeck U, Kleine Büning H. Learning Boolean Specifications. Artificial Intelligence. 2015:246-257. doi:10.1016/j.artint.2015.09.003 apa: Bubeck, U., & Kleine Büning, H. (2015). Learning Boolean Specifications. Artificial Intelligence, 246–257. https://doi.org/10.1016/j.artint.2015.09.003 bibtex: '@article{Bubeck_Kleine Büning_2015, title={Learning Boolean Specifications}, DOI={10.1016/j.artint.2015.09.003}, journal={Artificial Intelligence}, publisher={Elsevier}, author={Bubeck, Uwe and Kleine Büning, Hans}, year={2015}, pages={246–257} }' chicago: Bubeck, Uwe, and Hans Kleine Büning. “Learning Boolean Specifications.” Artificial Intelligence, 2015, 246–57. https://doi.org/10.1016/j.artint.2015.09.003. ieee: U. Bubeck and H. Kleine Büning, “Learning Boolean Specifications,” Artificial Intelligence, pp. 246–257, 2015. mla: Bubeck, Uwe, and Hans Kleine Büning. “Learning Boolean Specifications.” Artificial Intelligence, Elsevier, 2015, pp. 246–57, doi:10.1016/j.artint.2015.09.003. short: U. Bubeck, H. Kleine Büning, Artificial Intelligence (2015) 246–257. date_created: 2018-06-25T10:43:19Z date_updated: 2022-01-06T06:59:10Z ddc: - '000' department: - _id: '34' doi: 10.1016/j.artint.2015.09.003 keyword: - Query learning - Propositional logic language: - iso: eng page: 246 - 257 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: Artificial Intelligence publication_identifier: issn: - 0004-3702 publisher: Elsevier status: public title: Learning Boolean Specifications type: journal_article user_id: '315' year: '2015' ... --- _id: '315' abstract: - lang: eng text: In this paper, we introduce an approach for combining embedded systems with Service-oriented Computing techniques based on a concrete application scenario from the robotics domain. Our proposed Service-oriented Architecture allows for incorporating computational expensive functionality as services into a distributed computing environment. Furthermore, our framework facilitates a seamless integration of embedded systems such as robots as service providers into the computing environment. The entire communication is based on so-called recipes, which can be interpreted as autonomous messages that contain all necessary information for executing compositions of services. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Jan full_name: Jatzkowski, Jan last_name: Jatzkowski - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Jatzkowski J, Kleinjohann B. Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study. In: Proceedings of the 5th IFIP International Embedded Systems Symposium. ; 2015.' apa: Jungmann, A., Jatzkowski, J., & Kleinjohann, B. (2015). Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study. In Proceedings of the 5th IFIP International Embedded Systems Symposium. bibtex: '@inproceedings{Jungmann_Jatzkowski_Kleinjohann_2015, title={Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study}, booktitle={Proceedings of the 5th IFIP International Embedded Systems Symposium}, author={Jungmann, Alexander and Jatzkowski, Jan and Kleinjohann, Bernd}, year={2015} }' chicago: Jungmann, Alexander, Jan Jatzkowski, and Bernd Kleinjohann. “Combining Service-Oriented Computing with Embedded Systems - A Robotics Case Study.” In Proceedings of the 5th IFIP International Embedded Systems Symposium, 2015. ieee: A. Jungmann, J. Jatzkowski, and B. Kleinjohann, “Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study,” in Proceedings of the 5th IFIP International Embedded Systems Symposium, 2015. mla: Jungmann, Alexander, et al. “Combining Service-Oriented Computing with Embedded Systems - A Robotics Case Study.” Proceedings of the 5th IFIP International Embedded Systems Symposium, 2015. short: 'A. Jungmann, J. Jatzkowski, B. Kleinjohann, in: Proceedings of the 5th IFIP International Embedded Systems Symposium, 2015.' date_created: 2017-10-17T12:41:53Z date_updated: 2022-01-06T06:58:58Z ddc: - '040' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:43:26Z date_updated: 2018-03-20T07:43:26Z file_id: '1436' file_name: 315-JungmannJatzkowskiKleinjohann.pdf file_size: 1482481 relation: main_file success: 1 file_date_updated: 2018-03-20T07:43:26Z has_accepted_license: '1' project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 5th IFIP International Embedded Systems Symposium status: public title: Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study type: conference user_id: '15504' year: '2015' ... --- _id: '319' abstract: - lang: eng text: Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prepends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches. author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Hans full_name: Kleine Büning, Hans last_name: Kleine Büning citation: ama: 'Mohr F, Jungmann A, Kleine Büning H. Automated Online Service Composition. In: Proceedings of the 12th IEEE International Conference on Services Computing (SCC). ; 2015:57--64. doi:10.1109/SCC.2015.18' apa: Mohr, F., Jungmann, A., & Kleine Büning, H. (2015). Automated Online Service Composition. In Proceedings of the 12th IEEE International Conference on Services Computing (SCC) (pp. 57--64). https://doi.org/10.1109/SCC.2015.18 bibtex: '@inproceedings{Mohr_Jungmann_Kleine Büning_2015, title={Automated Online Service Composition}, DOI={10.1109/SCC.2015.18}, booktitle={Proceedings of the 12th IEEE International Conference on Services Computing (SCC)}, author={Mohr, Felix and Jungmann, Alexander and Kleine Büning, Hans}, year={2015}, pages={57--64} }' chicago: Mohr, Felix, Alexander Jungmann, and Hans Kleine Büning. “Automated Online Service Composition.” In Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 57--64, 2015. https://doi.org/10.1109/SCC.2015.18. ieee: F. Mohr, A. Jungmann, and H. Kleine Büning, “Automated Online Service Composition,” in Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64. mla: Mohr, Felix, et al. “Automated Online Service Composition.” Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64, doi:10.1109/SCC.2015.18. short: 'F. Mohr, A. Jungmann, H. Kleine Büning, in: Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64.' date_created: 2017-10-17T12:41:54Z date_updated: 2022-01-06T06:59:04Z ddc: - '040' department: - _id: '355' doi: 10.1109/SCC.2015.18 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:42:03Z date_updated: 2018-03-20T07:42:03Z file_id: '1434' file_name: 319-07207336.pdf file_size: 345742 relation: main_file success: 1 file_date_updated: 2018-03-20T07:42:03Z has_accepted_license: '1' language: - iso: eng page: 57--64 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 12th IEEE International Conference on Services Computing (SCC) status: public title: Automated Online Service Composition type: conference user_id: '477' year: '2015' ... --- _id: '272' abstract: - lang: eng text: 'Automated service composition aims at automatically generating software solutions based on services to provide more complex functionality. In this paper, we give an initial overview about why adaptivity becomes increasingly important when aiming for automated composition of service functionality in dynamic and freely accessible environments such as service markets. We systematically derive dependencies among crucial processes such as service composition and service execution in a holistic view. Furthermore, we briefly discuss the influences and effects of changes in the environment according to the derived dependencies, and derive possible future research directions. ' author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann citation: ama: 'Jungmann A. On Adaptivity for Automated Composition of Service Functionality. In: Proceedings of the IEEE 11th World Congress on Services (SERVICES). ; 2015:329--332. doi:10.1109/SERVICES.2015.57' apa: Jungmann, A. (2015). On Adaptivity for Automated Composition of Service Functionality. In Proceedings of the IEEE 11th World Congress on Services (SERVICES) (pp. 329--332). https://doi.org/10.1109/SERVICES.2015.57 bibtex: '@inproceedings{Jungmann_2015, title={On Adaptivity for Automated Composition of Service Functionality}, DOI={10.1109/SERVICES.2015.57}, booktitle={Proceedings of the IEEE 11th World Congress on Services (SERVICES)}, author={Jungmann, Alexander}, year={2015}, pages={329--332} }' chicago: Jungmann, Alexander. “On Adaptivity for Automated Composition of Service Functionality.” In Proceedings of the IEEE 11th World Congress on Services (SERVICES), 329--332, 2015. https://doi.org/10.1109/SERVICES.2015.57. ieee: A. Jungmann, “On Adaptivity for Automated Composition of Service Functionality,” in Proceedings of the IEEE 11th World Congress on Services (SERVICES), 2015, pp. 329--332. mla: Jungmann, Alexander. “On Adaptivity for Automated Composition of Service Functionality.” Proceedings of the IEEE 11th World Congress on Services (SERVICES), 2015, pp. 329--332, doi:10.1109/SERVICES.2015.57. short: 'A. Jungmann, in: Proceedings of the IEEE 11th World Congress on Services (SERVICES), 2015, pp. 329--332.' date_created: 2017-10-17T12:41:45Z date_updated: 2022-01-06T06:57:36Z ddc: - '040' doi: 10.1109/SERVICES.2015.57 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T09:29:05Z date_updated: 2018-03-21T09:29:05Z file_id: '1474' file_name: 272-07196545.pdf file_size: 163452 relation: main_file success: 1 file_date_updated: 2018-03-21T09:29:05Z has_accepted_license: '1' page: 329--332 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the IEEE 11th World Congress on Services (SERVICES) status: public title: On Adaptivity for Automated Composition of Service Functionality type: conference user_id: '15504' year: '2015' ... --- _id: '345' abstract: - lang: eng text: Automatically composing service-based software solutions is a challenging task. Considering context information during this service composition process is even more challenging. In domains such as image processing, however, context-sensitivity is inherent and cannot be ignored when developing techniques for automatic service composition. Formal approaches tend to create ambiguous solutions, whenever the expressive power of the applied formalism is limited. For example, services may have the same formal specification, although their actual functionality depends on the concrete context. In order to satisfy individual user requests while providing data-dependent functionality, formal approaches have to be extended. We propose to incorporate Reinforcement Learning techniques and combine them with planning based composition approaches. While planning ensures formally correct solutions, learning enables the composition process to resolve ambiguity by implicitly considering context information. Preliminary results show that our combined approach adapts to a static context while still satisfying formally specified requirements. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Kleinjohann B. Towards Context-Sensitive Service Composition for Service-Oriented Image Processing. In: Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom). ; 2014:755-758. doi:10.1109/CloudCom.2014.154' apa: Jungmann, A., & Kleinjohann, B. (2014). Towards Context-Sensitive Service Composition for Service-Oriented Image Processing. In Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 755–758). https://doi.org/10.1109/CloudCom.2014.154 bibtex: '@inproceedings{Jungmann_Kleinjohann_2014, title={Towards Context-Sensitive Service Composition for Service-Oriented Image Processing}, DOI={10.1109/CloudCom.2014.154}, booktitle={Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom)}, author={Jungmann, Alexander and Kleinjohann, Bernd}, year={2014}, pages={755–758} }' chicago: Jungmann, Alexander, and Bernd Kleinjohann. “Towards Context-Sensitive Service Composition for Service-Oriented Image Processing.” In Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom), 755–58, 2014. https://doi.org/10.1109/CloudCom.2014.154. ieee: A. Jungmann and B. Kleinjohann, “Towards Context-Sensitive Service Composition for Service-Oriented Image Processing,” in Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom), 2014, pp. 755–758. mla: Jungmann, Alexander, and Bernd Kleinjohann. “Towards Context-Sensitive Service Composition for Service-Oriented Image Processing.” Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom), 2014, pp. 755–58, doi:10.1109/CloudCom.2014.154. short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom), 2014, pp. 755–758.' date_created: 2017-10-17T12:41:59Z date_updated: 2022-01-06T06:59:17Z ddc: - '040' doi: 10.1109/CloudCom.2014.154 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:27:24Z date_updated: 2018-03-20T07:27:24Z file_id: '1419' file_name: 345-cloudcom2014-Jungmann.pdf file_size: 508878 relation: main_file success: 1 file_date_updated: 2018-03-20T07:27:24Z has_accepted_license: '1' page: 755-758 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom) status: public title: Towards Context-Sensitive Service Composition for Service-Oriented Image Processing type: conference user_id: '15504' year: '2014' ... --- _id: '346' abstract: - lang: eng text: One future goal of service-oriented computing is to realize global markets of composed services. On such markets, service providers offer services that can be flexibly combined with each other. However, most often, market participants are not able to individually estimate the quality of traded services in advance. As a consequence, even potentially profitable transactions between customers and providers might not take place. In the worst case, this can induce a market failure. To overcome this problem, we propose the incorporation of reputation information as an indicator for expected service quality. We address On-The-Fly Computing as a representative environment of markets of composed services. In this environment, customers provide feedback on transactions. We present a conceptual design of a reputation system which collects and processes user feedback, and provides it to participants in the market. Our contribution includes the identification of requirements for such a reputation system from a technical and an economic perspective. Based on these requirements, we propose a flexible solution that facilitates the incorporation of reputation information into markets of composed services while simultaneously preserving privacy of customers who provide feedback. The requirements we formulate in this paper have just been partially met in literature. An integrated approach, however, has not been addressed yet. author: - first_name: Sonja full_name: Brangewitz, Sonja last_name: Brangewitz - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Ronald full_name: Petrlic, Ronald last_name: Petrlic - first_name: Marie Christin full_name: Platenius, Marie Christin last_name: Platenius citation: ama: 'Brangewitz S, Jungmann A, Petrlic R, Platenius MC. Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services. In: Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION). ; 2014:49-57.' apa: Brangewitz, S., Jungmann, A., Petrlic, R., & Platenius, M. C. (2014). Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services. In Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 49–57). bibtex: '@inproceedings{Brangewitz_Jungmann_Petrlic_Platenius_2014, title={Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services}, booktitle={Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}, author={Brangewitz, Sonja and Jungmann, Alexander and Petrlic, Ronald and Platenius, Marie Christin}, year={2014}, pages={49–57} }' chicago: Brangewitz, Sonja, Alexander Jungmann, Ronald Petrlic, and Marie Christin Platenius. “Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services.” In Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 49–57, 2014. ieee: S. Brangewitz, A. Jungmann, R. Petrlic, and M. C. Platenius, “Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services,” in Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2014, pp. 49–57. mla: Brangewitz, Sonja, et al. “Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services.” Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2014, pp. 49–57. short: 'S. Brangewitz, A. Jungmann, R. Petrlic, M.C. Platenius, in: Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2014, pp. 49–57.' date_created: 2017-10-17T12:41:59Z date_updated: 2022-01-06T06:59:18Z ddc: - '040' department: - _id: '205' - _id: '76' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:26:49Z date_updated: 2018-03-20T07:26:49Z file_id: '1418' file_name: 346-service_computation_2014_3_10_10005.pdf file_size: 334101 relation: main_file success: 1 file_date_updated: 2018-03-20T07:26:49Z has_accepted_license: '1' language: - iso: eng page: 49-57 project: - _id: '1' name: SFB 901 - _id: '13' name: SFB 901 - Subprojekt C1 - _id: '9' name: SFB 901 - Subprojekt B1 - _id: '7' name: SFB 901 - Subprojekt A3 - _id: '10' name: SFB 901 - Subproject B2 - _id: '2' name: SFB 901 - Project Area A - _id: '3' name: SFB 901 - Project Area B - _id: '4' name: SFB 901 - Project Area C publication: Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION) status: public title: Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services type: conference user_id: '477' year: '2014' ... --- _id: '353' abstract: - lang: eng text: 'There are many technologies for the automation of processesthat deal with services; examples are service discovery and composition.Automation of these processes requires that the services are described semantically. However, semantically described services are currently not oronly rarely available, which limits the applicability of discovery and composition approaches. The systematic support for creating new semanticservices usable by automated technologies is an open problem.We tackle this problem with a template based approach: Domain independent templates are instantiated with domain specific services andboolean expressions. The obtained services have semantic descriptionswhose correctness directly follows from the correctness of the template.Besides the theory, we present experimental results for a service repository in which 85% of the services were generated automatically.' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Sven full_name: Walther, Sven last_name: Walther citation: ama: 'Mohr F, Walther S. Template-based Generation of Semantic Services. In: Proceedings of the 14th International Conference on Software Reuse (ICSR). LNCS. ; 2014:188-203. doi:10.1007/978-3-319-14130-5_14' apa: Mohr, F., & Walther, S. (2014). Template-based Generation of Semantic Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 188–203). https://doi.org/10.1007/978-3-319-14130-5_14 bibtex: '@inproceedings{Mohr_Walther_2014, series={LNCS}, title={Template-based Generation of Semantic Services}, DOI={10.1007/978-3-319-14130-5_14}, booktitle={Proceedings of the 14th International Conference on Software Reuse (ICSR)}, author={Mohr, Felix and Walther, Sven}, year={2014}, pages={188–203}, collection={LNCS} }' chicago: Mohr, Felix, and Sven Walther. “Template-Based Generation of Semantic Services.” In Proceedings of the 14th International Conference on Software Reuse (ICSR), 188–203. LNCS, 2014. https://doi.org/10.1007/978-3-319-14130-5_14. ieee: F. Mohr and S. Walther, “Template-based Generation of Semantic Services,” in Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203. mla: Mohr, Felix, and Sven Walther. “Template-Based Generation of Semantic Services.” Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203, doi:10.1007/978-3-319-14130-5_14. short: 'F. Mohr, S. Walther, in: Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203.' date_created: 2017-10-17T12:42:00Z date_updated: 2022-01-06T06:59:22Z ddc: - '040' department: - _id: '77' - _id: '355' doi: 10.1007/978-3-319-14130-5_14 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:23:32Z date_updated: 2018-03-20T07:23:32Z file_id: '1414' file_name: 353-icsr2015_submission_17.pdf file_size: 431778 relation: main_file success: 1 file_date_updated: 2018-03-20T07:23:32Z has_accepted_license: '1' language: - iso: eng page: 188-203 project: - _id: '1' name: SFB 901 - _id: '11' name: SFB 901 - Subprojekt B3 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: Proceedings of the 14th International Conference on Software Reuse (ICSR) series_title: LNCS status: public title: Template-based Generation of Semantic Services type: conference user_id: '477' year: '2014' ... --- _id: '366' abstract: - lang: eng text: On-The-Fly (OTF) Computing constitutes an approach towards highly dynamic and individualized software markets. Based on service-oriented computing, OTF Computing is about realizing global markets of services that can be flexibly combined. We report on our current research activities, the security and privacy implications thereof, and our approaches to tackle the challenges. Furthermore, we discuss how the security and privacy challenges are addressed in research projects similar to OTF Computing. author: - first_name: Ronald full_name: Petrlic, Ronald last_name: Petrlic - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Marie Christin full_name: Platenius, Marie Christin last_name: Platenius - first_name: Wilhelm full_name: Schäfer, Wilhelm last_name: Schäfer - first_name: Christoph full_name: Sorge, Christoph last_name: Sorge citation: ama: 'Petrlic R, Jungmann A, Platenius MC, Schäfer W, Sorge C. Security and Privacy Challenges in On-The-Fly Computing. In: Tagungsband Der 4. Konferenz Software-Technologien Und -Prozesse (STeP 2014). ; 2014:131-142.' apa: Petrlic, R., Jungmann, A., Platenius, M. C., Schäfer, W., & Sorge, C. (2014). Security and Privacy Challenges in On-The-Fly Computing. In Tagungsband der 4. Konferenz Software-Technologien und -Prozesse (STeP 2014) (pp. 131–142). bibtex: '@inproceedings{Petrlic_Jungmann_Platenius_Schäfer_Sorge_2014, title={Security and Privacy Challenges in On-The-Fly Computing}, booktitle={Tagungsband der 4. Konferenz Software-Technologien und -Prozesse (STeP 2014)}, author={Petrlic, Ronald and Jungmann, Alexander and Platenius, Marie Christin and Schäfer, Wilhelm and Sorge, Christoph}, year={2014}, pages={131–142} }' chicago: Petrlic, Ronald, Alexander Jungmann, Marie Christin Platenius, Wilhelm Schäfer, and Christoph Sorge. “Security and Privacy Challenges in On-The-Fly Computing.” In Tagungsband Der 4. Konferenz Software-Technologien Und -Prozesse (STeP 2014), 131–42, 2014. ieee: R. Petrlic, A. Jungmann, M. C. Platenius, W. Schäfer, and C. Sorge, “Security and Privacy Challenges in On-The-Fly Computing,” in Tagungsband der 4. Konferenz Software-Technologien und -Prozesse (STeP 2014), 2014, pp. 131–142. mla: Petrlic, Ronald, et al. “Security and Privacy Challenges in On-The-Fly Computing.” Tagungsband Der 4. Konferenz Software-Technologien Und -Prozesse (STeP 2014), 2014, pp. 131–42. short: 'R. Petrlic, A. Jungmann, M.C. Platenius, W. Schäfer, C. Sorge, in: Tagungsband Der 4. Konferenz Software-Technologien Und -Prozesse (STeP 2014), 2014, pp. 131–142.' date_created: 2017-10-17T12:42:03Z date_updated: 2022-01-06T06:59:29Z ddc: - '040' department: - _id: '76' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-20T07:18:47Z date_updated: 2018-03-20T07:18:47Z file_id: '1405' file_name: 366-otf-security-privacy-final.pdf file_size: 74488 relation: main_file success: 1 file_date_updated: 2018-03-20T07:18:47Z has_accepted_license: '1' language: - iso: eng page: 131-142 project: - _id: '1' name: SFB 901 - _id: '9' name: SFB 901 - Subprojekt B1 - _id: '13' name: SFB 901 - Subprojekt C1 - _id: '3' name: SFB 901 - Project Area B - _id: '4' name: SFB 901 - Project Area C - _id: '10' name: SFB 901 - Subproject B2 publication: Tagungsband der 4. Konferenz Software-Technologien und -Prozesse (STeP 2014) status: public title: Security and Privacy Challenges in On-The-Fly Computing type: conference user_id: '477' year: '2014' ... --- _id: '447' abstract: - lang: eng text: Automatic service composition is still a challengingtask. It is even more challenging when dealing witha dynamic market of services for end users. New servicesmay enter the market while other services are completelyremoved. Furthermore, end users are typically no experts in thedomain in which they formulate a request. As a consequence,ambiguous user requests will inevitably emerge and have tobe taken into account. To meet these challenges, we proposea new approach that combines automatic service compositionwith adaptive service recommendation. A best first backwardsearch algorithm produces solutions that are functional correctwith respect to user requests. An adaptive recommendationsystem supports the search algorithm in decision-making.Reinforcement Learning techniques enable the system to adjustits recommendation strategy over time based on user ratings.The integrated approach is described on a conceptional leveland demonstrated by means of an illustrative example fromthe image processing domain. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Mohr F, Kleinjohann B. Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. In: Proceedings of the 10th World Congress on Services (SERVICES). ; 2014:346-353. doi:10.1109/SERVICES.2014.68' apa: Jungmann, A., Mohr, F., & Kleinjohann, B. (2014). Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. In Proceedings of the 10th World Congress on Services (SERVICES) (pp. 346–353). https://doi.org/10.1109/SERVICES.2014.68 bibtex: '@inproceedings{Jungmann_Mohr_Kleinjohann_2014, title={Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services}, DOI={10.1109/SERVICES.2014.68}, booktitle={Proceedings of the 10th World Congress on Services (SERVICES)}, author={Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd}, year={2014}, pages={346–353} }' chicago: Jungmann, Alexander, Felix Mohr, and Bernd Kleinjohann. “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services.” In Proceedings of the 10th World Congress on Services (SERVICES), 346–53, 2014. https://doi.org/10.1109/SERVICES.2014.68. ieee: A. Jungmann, F. Mohr, and B. Kleinjohann, “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services,” in Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–353. mla: Jungmann, Alexander, et al. “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services.” Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–53, doi:10.1109/SERVICES.2014.68. short: 'A. Jungmann, F. Mohr, B. Kleinjohann, in: Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–353.' date_created: 2017-10-17T12:42:19Z date_updated: 2022-01-06T07:01:06Z ddc: - '040' department: - _id: '355' doi: 10.1109/SERVICES.2014.68 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-16T11:26:36Z date_updated: 2018-03-16T11:26:36Z file_id: '1347' file_name: 447-_FINAL__Combining_Automatic_Service_Composition_with_Adaptive_Service_Recommendation_for_Dynamic_Markets_of_Services.pdf file_size: 429462 relation: main_file success: 1 file_date_updated: 2018-03-16T11:26:36Z has_accepted_license: '1' language: - iso: eng page: 346-353 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 10th World Congress on Services (SERVICES) status: public title: Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services type: conference user_id: '477' year: '2014' ... --- _id: '457' abstract: - lang: eng text: Automatically composing service-based software solutionsis still a challenging task. Functional as well as nonfunctionalproperties have to be considered in order to satisfyindividual user requests. Regarding non-functional properties,the composition process can be modeled as optimization problemand solved accordingly. Functional properties, in turn, can bedescribed by means of a formal specification language. Statespacebased planning approaches can then be applied to solvethe underlying composition problem. However, depending on theexpressiveness of the applied formalism and the completenessof the functional descriptions, formally equivalent services maystill differ with respect to their implemented functionality. As aconsequence, the most appropriate solution for a desired functionalitycan hardly be determined without considering additionalinformation. In this paper, we demonstrate how to overcome thislack of information by means of Reinforcement Learning. Inorder to resolve ambiguity, we expand state-space based servicecomposition by a recommendation mechanism that supportsdecision-making beyond formal specifications. The recommendationmechanism adjusts its recommendation strategy basedon feedback from previous composition runs. Image processingserves as case study. Experimental results show the benefit of ourproposed solution. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: 'Bernd ' full_name: 'Kleinjohann, Bernd ' last_name: Kleinjohann citation: ama: 'Jungmann A, Mohr F, Kleinjohann B. Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. In: Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA). ; 2014:105-112. doi:10.1109/SOCA.2014.48' apa: Jungmann, A., Mohr, F., & Kleinjohann, B. (2014). Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. In Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 105–112). https://doi.org/10.1109/SOCA.2014.48 bibtex: '@inproceedings{Jungmann_Mohr_Kleinjohann_2014, title={Applying Reinforcement Learning for Resolving Ambiguity in Service Composition}, DOI={10.1109/SOCA.2014.48}, booktitle={Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA)}, author={Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd }, year={2014}, pages={105–112} }' chicago: Jungmann, Alexander, Felix Mohr, and Bernd Kleinjohann. “Applying Reinforcement Learning for Resolving Ambiguity in Service Composition.” In Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 105–12, 2014. https://doi.org/10.1109/SOCA.2014.48. ieee: A. Jungmann, F. Mohr, and B. Kleinjohann, “Applying Reinforcement Learning for Resolving Ambiguity in Service Composition,” in Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 2014, pp. 105–112. mla: Jungmann, Alexander, et al. “Applying Reinforcement Learning for Resolving Ambiguity in Service Composition.” Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 2014, pp. 105–12, doi:10.1109/SOCA.2014.48. short: 'A. Jungmann, F. Mohr, B. Kleinjohann, in: Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 2014, pp. 105–112.' date_created: 2017-10-17T12:42:21Z date_updated: 2022-01-06T07:01:12Z ddc: - '040' department: - _id: '355' doi: 10.1109/SOCA.2014.48 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-16T11:22:26Z date_updated: 2018-03-16T11:22:26Z file_id: '1339' file_name: 457-SOCA2014-Jungmann-Mohr.pdf file_size: 1324374 relation: main_file success: 1 file_date_updated: 2018-03-16T11:22:26Z has_accepted_license: '1' language: - iso: eng page: 105-112 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA) status: public title: Applying Reinforcement Learning for Resolving Ambiguity in Service Composition type: conference user_id: '477' year: '2014' ... --- _id: '407' abstract: - lang: eng text: Automated programming aims at automatically assembling a new software artifact from existing software modules. Although automated programming was revitalized through automated software composition in the last decade, the problem cannot be considered solved. Automated software composition is widely accepted as being a planning task, but the problem is that it has very special properties that other planning problems do not have and that are commonly overseen. These properties usually imply that the composition problem cannot be solved with standard planning tools. This paper gives a brief and intuitive description of the planning problem that most approaches are based on. It points out special properties of this problem and explains why it is not adequate to solve the problem with classical planning tools as done by most existing approaches. author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr citation: ama: 'Mohr F. Issues of Automated Software Composition in AI Planning. In: Proceedings of the 29th International Conference on Automated Software Engineering (ASE). ; 2014:895--898. doi:10.1145/2642937.2653470' apa: Mohr, F. (2014). Issues of Automated Software Composition in AI Planning. In Proceedings of the 29th International Conference on Automated Software Engineering (ASE) (pp. 895--898). https://doi.org/10.1145/2642937.2653470 bibtex: '@inproceedings{Mohr_2014, title={Issues of Automated Software Composition in AI Planning}, DOI={10.1145/2642937.2653470}, booktitle={Proceedings of the 29th International Conference on Automated Software Engineering (ASE)}, author={Mohr, Felix}, year={2014}, pages={895--898} }' chicago: Mohr, Felix. “Issues of Automated Software Composition in AI Planning.” In Proceedings of the 29th International Conference on Automated Software Engineering (ASE), 895--898, 2014. https://doi.org/10.1145/2642937.2653470. ieee: F. Mohr, “Issues of Automated Software Composition in AI Planning,” in Proceedings of the 29th International Conference on Automated Software Engineering (ASE), 2014, pp. 895--898. mla: Mohr, Felix. “Issues of Automated Software Composition in AI Planning.” Proceedings of the 29th International Conference on Automated Software Engineering (ASE), 2014, pp. 895--898, doi:10.1145/2642937.2653470. short: 'F. Mohr, in: Proceedings of the 29th International Conference on Automated Software Engineering (ASE), 2014, pp. 895--898.' date_created: 2017-10-17T12:42:11Z date_updated: 2022-01-06T07:00:12Z ddc: - '040' doi: 10.1145/2642937.2653470 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-16T11:36:04Z date_updated: 2018-03-16T11:36:04Z file_id: '1365' file_name: 407-ASE14.pdf file_size: 443283 relation: main_file success: 1 file_date_updated: 2018-03-16T11:36:04Z has_accepted_license: '1' page: ' 895--898' project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 29th International Conference on Automated Software Engineering (ASE) status: public title: Issues of Automated Software Composition in AI Planning type: conference user_id: '15504' year: '2014' ... --- _id: '410' abstract: - lang: eng text: One goal of service-oriented computing is to realize future markets of composed services. In such markets, service providers offer services that can be flexibly combined with each other. However, although crucial for decision-making, market participants are usually not able to individually estimate the quality of traded services in advance. To overcome this problem, we present a conceptual design for a reputation system that collects and processes user feedback on transactions, and provides this information as a signal for quality to participants in the market. Based on our proposed concept, we describe the incorporation of reputation information into distinct decision-making processes that are crucial in such service markets. In this context, we present a fuzzy service matching approach that takes reputation information into account. Furthermore, we introduce an adaptive service composition approach, and investigate the impact of exchanging immediate user feedback by reputation information. Last but not least, we describe the importance of reputation information for economic decisions of different market participants. The overall output of this paper is a comprehensive view on managing and exploiting reputation information in markets of composed services using the example of On-The-Fly Computing. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Sonja full_name: Brangewitz, Sonja last_name: Brangewitz - first_name: Ronald full_name: Petrlic, Ronald last_name: Petrlic - first_name: Marie Christin full_name: Platenius, Marie Christin last_name: Platenius citation: ama: Jungmann A, Brangewitz S, Petrlic R, Platenius MC. Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services. International Journal On Advances in Intelligent Systems (IntSys). 2014;7(3&4):572--594. apa: Jungmann, A., Brangewitz, S., Petrlic, R., & Platenius, M. C. (2014). Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services. International Journal On Advances in Intelligent Systems (IntSys), 7(3&4), 572--594. bibtex: '@article{Jungmann_Brangewitz_Petrlic_Platenius_2014, title={Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services}, volume={7}, number={3&4}, journal={International Journal On Advances in Intelligent Systems (IntSys)}, publisher={IARIA}, author={Jungmann, Alexander and Brangewitz, Sonja and Petrlic, Ronald and Platenius, Marie Christin}, year={2014}, pages={572--594} }' chicago: 'Jungmann, Alexander, Sonja Brangewitz, Ronald Petrlic, and Marie Christin Platenius. “Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services.” International Journal On Advances in Intelligent Systems (IntSys) 7, no. 3&4 (2014): 572--594.' ieee: A. Jungmann, S. Brangewitz, R. Petrlic, and M. C. Platenius, “Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services,” International Journal On Advances in Intelligent Systems (IntSys), vol. 7, no. 3&4, pp. 572--594, 2014. mla: Jungmann, Alexander, et al. “Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services.” International Journal On Advances in Intelligent Systems (IntSys), vol. 7, no. 3&4, IARIA, 2014, pp. 572--594. short: A. Jungmann, S. Brangewitz, R. Petrlic, M.C. Platenius, International Journal On Advances in Intelligent Systems (IntSys) 7 (2014) 572--594. date_created: 2017-10-17T12:42:11Z date_updated: 2022-01-06T07:00:17Z ddc: - '040' department: - _id: '205' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-16T11:34:28Z date_updated: 2018-03-16T11:34:28Z file_id: '1362' file_name: 410-intsys_v7_n34_2014_18.pdf file_size: 2590608 relation: main_file success: 1 file_date_updated: 2018-03-16T11:34:28Z has_accepted_license: '1' intvolume: ' 7' issue: 3&4 language: - iso: eng main_file_link: - url: http://www.iariajournals.org/intelligent_systems/intsys_v7_n34_2014_paged.pdf page: 572--594 project: - _id: '1' name: SFB 901 - _id: '7' name: SFB 901 - Subprojekt A3 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '13' name: SFB 901 - Subprojekt C1 - _id: '2' name: SFB 901 - Project Area A - _id: '4' name: SFB 901 - Project Area C - _id: '3' name: SFB 901 - Project Area B publication: International Journal On Advances in Intelligent Systems (IntSys) publisher: IARIA status: public title: Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services type: journal_article user_id: '65453' volume: 7 year: '2014' ... --- _id: '425' abstract: - lang: eng text: In this paper, we evaluate the robustness of our color-based segmentation approach in combination with different color spaces, namely RGB, L*a*b*, HSV, and log-chromaticity (LCCS). For this purpose, we describe our deterministic segmentation algorithm including its gradually transformation of pixel-precise image data into a less error-prone and therefore more robust statistical representation in terms of moments. To investigate the robustness of a specific segmentation setting, we introduce our evaluation framework that directly works on the statistical representation. It is based on two different types of robustness measures, namely relative and absolute robustness. While relative robustness measures stability of segmentation results over time, absolute robustness measures stability regarding varying illumination by comparing results with ground truth data. The significance of these robustness measures is shown by evaluating our segmentation approach with different color spaces. For the evaluation process, an artificial scene was chosen as representative for application scenarios based on artificial landmarks. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Jan full_name: Jatzkowski, Jan last_name: Jatzkowski - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Jatzkowski J, Kleinjohann B. Evaluation of Color Spaces for Robust Image Segmentation. In: Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP). ; 2014:648-655.' apa: Jungmann, A., Jatzkowski, J., & Kleinjohann, B. (2014). Evaluation of Color Spaces for Robust Image Segmentation. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP) (pp. 648–655). bibtex: '@inproceedings{Jungmann_Jatzkowski_Kleinjohann_2014, title={Evaluation of Color Spaces for Robust Image Segmentation}, booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP)}, author={Jungmann, Alexander and Jatzkowski, Jan and Kleinjohann, Bernd}, year={2014}, pages={648–655} }' chicago: Jungmann, Alexander, Jan Jatzkowski, and Bernd Kleinjohann. “Evaluation of Color Spaces for Robust Image Segmentation.” In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP), 648–55, 2014. ieee: A. Jungmann, J. Jatzkowski, and B. Kleinjohann, “Evaluation of Color Spaces for Robust Image Segmentation,” in Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP), 2014, pp. 648–655. mla: Jungmann, Alexander, et al. “Evaluation of Color Spaces for Robust Image Segmentation.” Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP), 2014, pp. 648–55. short: 'A. Jungmann, J. Jatzkowski, B. Kleinjohann, in: Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP), 2014, pp. 648–655.' date_created: 2017-10-17T12:42:14Z date_updated: 2022-01-06T07:00:42Z ddc: - '040' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-16T11:32:35Z date_updated: 2018-03-16T11:32:35Z file_id: '1358' file_name: 425-visapp2014-camera-ready.pdf file_size: 6355291 relation: main_file success: 1 file_date_updated: 2018-03-16T11:32:35Z has_accepted_license: '1' page: 648-655 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP) status: public title: Evaluation of Color Spaces for Robust Image Segmentation type: conference user_id: '15504' year: '2014' ... --- _id: '428' abstract: - lang: eng text: Services are self-contained software components that can be used platform independent and that aim at maximizing software reuse. A basic concern in service oriented architectures is to measure the reusability of services. One of the most important qualities is the functional reusability, which indicates how relevant the task is that a service solves. Current metrics for functional reusability of software, however, either require source code analysis or have very little explanatory power. This paper gives a formally described vision statement for the estimation of functional reusability of services and sketches an exemplary reusability metric that is based on the service descriptions. author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr citation: ama: 'Mohr F. Estimating Functional Reusability of Services. In: Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC). LNCS. ; 2014:411-418.' apa: Mohr, F. (2014). Estimating Functional Reusability of Services. In Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC) (pp. 411–418). bibtex: '@inproceedings{Mohr_2014, series={LNCS}, title={Estimating Functional Reusability of Services}, booktitle={Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC)}, author={Mohr, Felix}, year={2014}, pages={411–418}, collection={LNCS} }' chicago: Mohr, Felix. “Estimating Functional Reusability of Services.” In Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 411–18. LNCS, 2014. ieee: F. Mohr, “Estimating Functional Reusability of Services,” in Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 2014, pp. 411–418. mla: Mohr, Felix. “Estimating Functional Reusability of Services.” Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 2014, pp. 411–18. short: 'F. Mohr, in: Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 2014, pp. 411–418.' date_created: 2017-10-17T12:42:15Z date_updated: 2022-01-06T07:00:47Z ddc: - '040' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-16T11:32:02Z date_updated: 2018-03-16T11:32:02Z file_id: '1357' file_name: 428-ICSOC14.pdf file_size: 200063 relation: main_file success: 1 file_date_updated: 2018-03-16T11:32:02Z has_accepted_license: '1' language: - iso: eng main_file_link: - url: https://link.springer.com/chapter/10.1007%2F978-3-662-45391-9_31 page: 411-418 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC) series_title: LNCS status: public title: Estimating Functional Reusability of Services type: conference user_id: '477' year: '2014' ... --- _id: '485' abstract: - lang: eng text: Software composition has been studied as a subject of state based planning for decades. Existing composition approaches that are efficient enough to be used in practice are limited to sequential arrangements of software components. This restriction dramatically reduces the number of composition problems that can be solved. However, there are many composition problems that could be solved by existing approaches if they had a possibility to combine components in very simple non-sequential ways. To this end, we present an approach that arranges not only basic components but also composite components. Composite components enhance the structure of the composition by conditional control flows. Through algorithms that are written by experts, composite components are automatically generated before the composition process starts. Therefore, our approach is not a substitute for existing composition algorithms but complements them with a preprocessing step. We verified the validity of our approach through implementation of the presented algorithms. author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Hans full_name: Kleine Büning, Hans last_name: Kleine Büning citation: ama: 'Mohr F, Kleine Büning H. Semi-Automated Software Composition Through Generated Components. In: Proceedings of the 15th International Conference on Information Integration and Web-Based Applications & Services (IiWAS). ; 2013:676-680. doi:10.1145/2539150.2539235' apa: Mohr, F., & Kleine Büning, H. (2013). Semi-Automated Software Composition Through Generated Components. In Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS) (pp. 676–680). https://doi.org/10.1145/2539150.2539235 bibtex: '@inproceedings{Mohr_Kleine Büning_2013, title={Semi-Automated Software Composition Through Generated Components}, DOI={10.1145/2539150.2539235}, booktitle={Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS)}, author={Mohr, Felix and Kleine Büning, Hans}, year={2013}, pages={676–680} }' chicago: Mohr, Felix, and Hans Kleine Büning. “Semi-Automated Software Composition Through Generated Components.” In Proceedings of the 15th International Conference on Information Integration and Web-Based Applications & Services (IiWAS), 676–80, 2013. https://doi.org/10.1145/2539150.2539235. ieee: F. Mohr and H. Kleine Büning, “Semi-Automated Software Composition Through Generated Components,” in Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS), 2013, pp. 676–680. mla: Mohr, Felix, and Hans Kleine Büning. “Semi-Automated Software Composition Through Generated Components.” Proceedings of the 15th International Conference on Information Integration and Web-Based Applications & Services (IiWAS), 2013, pp. 676–80, doi:10.1145/2539150.2539235. short: 'F. Mohr, H. Kleine Büning, in: Proceedings of the 15th International Conference on Information Integration and Web-Based Applications & Services (IiWAS), 2013, pp. 676–680.' date_created: 2017-10-17T12:42:26Z date_updated: 2022-01-06T07:01:27Z ddc: - '040' department: - _id: '355' doi: 10.1145/2539150.2539235 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-15T13:56:49Z date_updated: 2018-03-15T13:56:49Z file_id: '1318' file_name: 485-paper86_mohr.pdf file_size: 368152 relation: main_file success: 1 file_date_updated: 2018-03-15T13:56:49Z has_accepted_license: '1' language: - iso: eng page: 676-680 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS) status: public title: Semi-Automated Software Composition Through Generated Components type: conference user_id: '477' year: '2013' ... --- _id: '495' abstract: - lang: eng text: Automated service composition has been studied as a subject of state based planning for a decade. A great deal of service composition tasks can only be solved if concrete output values of the services are considered in the composition process. However, the fact that those values are not known before runtime leads to nondeterministic planning problems, which have proven to be notoriously difficult in practical automated service composition applications. Even though this problem is frequently recognized, it has still received remarkably few attention and remains unsolved.This paper shows how nondeterminism in automated service composition can be reduced. We introduce context rules as a means to derive semantic knowledge from output values of services. These rules enable us to replace nondeterministic composition operations by less nondeterministic or even completely deterministic ones. We show the validity of our solutions not only theoretically but also have evaluated them practically through implementation. author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Theodor full_name: Lettmann, Theodor id: '315' last_name: Lettmann orcid: 0000-0001-5859-2457 - first_name: Hans full_name: Kleine Büning, Hans last_name: Kleine Büning citation: ama: 'Mohr F, Lettmann T, Kleine Büning H. Reducing Nondeterminism in Automated Service Composition. In: Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA). ; 2013:154-161. doi:10.1109/SOCA.2013.25' apa: Mohr, F., Lettmann, T., & Kleine Büning, H. (2013). Reducing Nondeterminism in Automated Service Composition. In Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 154–161). https://doi.org/10.1109/SOCA.2013.25 bibtex: '@inproceedings{Mohr_Lettmann_Kleine Büning_2013, title={Reducing Nondeterminism in Automated Service Composition}, DOI={10.1109/SOCA.2013.25}, booktitle={Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA)}, author={Mohr, Felix and Lettmann, Theodor and Kleine Büning, Hans}, year={2013}, pages={154–161} }' chicago: Mohr, Felix, Theodor Lettmann, and Hans Kleine Büning. “Reducing Nondeterminism in Automated Service Composition.” In Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 154–61, 2013. https://doi.org/10.1109/SOCA.2013.25. ieee: F. Mohr, T. Lettmann, and H. Kleine Büning, “Reducing Nondeterminism in Automated Service Composition,” in Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 2013, pp. 154–161. mla: Mohr, Felix, et al. “Reducing Nondeterminism in Automated Service Composition.” Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 2013, pp. 154–61, doi:10.1109/SOCA.2013.25. short: 'F. Mohr, T. Lettmann, H. Kleine Büning, in: Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 2013, pp. 154–161.' date_created: 2017-10-17T12:42:28Z date_updated: 2022-01-06T07:01:30Z ddc: - '040' department: - _id: '355' doi: 10.1109/SOCA.2013.25 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-15T13:43:08Z date_updated: 2018-03-15T13:43:08Z file_id: '1314' file_name: 495-paper52_mohr.pdf file_size: 603822 relation: main_file success: 1 file_date_updated: 2018-03-15T13:43:08Z has_accepted_license: '1' language: - iso: eng page: 154-161 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA) status: public title: Reducing Nondeterminism in Automated Service Composition type: conference user_id: '477' year: '2013' ... --- _id: '515' abstract: - lang: eng text: The as a service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilised on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our approach of modelling this service composition and recommendation process as Markov decision process and of solving it by means of reinforcement learning. A case study serves as proof of concept. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann - first_name: Elisabeth full_name: Kleinjohann, Elisabeth id: '15588' last_name: Kleinjohann citation: ama: Jungmann A, Kleinjohann B, Kleinjohann E. Learning Service Recommendations. International Journal of Business Process Integration and Management. 2013;(4):284-297. doi:10.1504/IJBPIM.2013.059135 apa: Jungmann, A., Kleinjohann, B., & Kleinjohann, E. (2013). Learning Service Recommendations. International Journal of Business Process Integration and Management, (4), 284–297. https://doi.org/10.1504/IJBPIM.2013.059135 bibtex: '@article{Jungmann_Kleinjohann_Kleinjohann_2013, title={Learning Service Recommendations}, DOI={10.1504/IJBPIM.2013.059135}, number={4}, journal={International Journal of Business Process Integration and Management}, publisher={InderScience}, author={Jungmann, Alexander and Kleinjohann, Bernd and Kleinjohann, Elisabeth}, year={2013}, pages={284–297} }' chicago: 'Jungmann, Alexander, Bernd Kleinjohann, and Elisabeth Kleinjohann. “Learning Service Recommendations.” International Journal of Business Process Integration and Management, no. 4 (2013): 284–97. https://doi.org/10.1504/IJBPIM.2013.059135.' ieee: A. Jungmann, B. Kleinjohann, and E. Kleinjohann, “Learning Service Recommendations,” International Journal of Business Process Integration and Management, no. 4, pp. 284–297, 2013. mla: Jungmann, Alexander, et al. “Learning Service Recommendations.” International Journal of Business Process Integration and Management, no. 4, InderScience, 2013, pp. 284–97, doi:10.1504/IJBPIM.2013.059135. short: A. Jungmann, B. Kleinjohann, E. Kleinjohann, International Journal of Business Process Integration and Management (2013) 284–297. date_created: 2017-10-17T12:42:32Z date_updated: 2022-01-06T07:01:39Z ddc: - '040' doi: 10.1504/IJBPIM.2013.059135 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-15T10:43:38Z date_updated: 2018-03-15T10:43:38Z file_id: '1301' file_name: 515-IJBPIM060402_JUNGMANN.pdf file_size: 5301831 relation: main_file success: 1 file_date_updated: 2018-03-15T10:43:38Z has_accepted_license: '1' issue: '4' main_file_link: - url: http://www.inderscience.com/offer.php?id=59135 page: 284-297 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: International Journal of Business Process Integration and Management publisher: InderScience status: public title: Learning Service Recommendations type: journal_article user_id: '477' year: '2013' ... --- _id: '516' abstract: - lang: eng text: 'The as a Service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilized on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our general idea of modeling this service composition and recommendation process as Markov Decision Process and of solving it by means of Reinforcement Learning. A case study serves as proof of concept. ' author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Kleinjohann B. Learning Recommendation System for Automated Service Composition. In: Proceedings of the 10th IEEE International Conference on Services Computing (SCC). ; 2013:97-104. doi:10.1109/SCC.2013.66' apa: Jungmann, A., & Kleinjohann, B. (2013). Learning Recommendation System for Automated Service Composition. In Proceedings of the 10th IEEE International Conference on Services Computing (SCC) (pp. 97–104). https://doi.org/10.1109/SCC.2013.66 bibtex: '@inproceedings{Jungmann_Kleinjohann_2013, title={Learning Recommendation System for Automated Service Composition}, DOI={10.1109/SCC.2013.66}, booktitle={Proceedings of the 10th IEEE International Conference on Services Computing (SCC)}, author={Jungmann, Alexander and Kleinjohann, Bernd}, year={2013}, pages={97–104} }' chicago: Jungmann, Alexander, and Bernd Kleinjohann. “Learning Recommendation System for Automated Service Composition.” In Proceedings of the 10th IEEE International Conference on Services Computing (SCC), 97–104, 2013. https://doi.org/10.1109/SCC.2013.66. ieee: A. Jungmann and B. Kleinjohann, “Learning Recommendation System for Automated Service Composition,” in Proceedings of the 10th IEEE International Conference on Services Computing (SCC), 2013, pp. 97–104. mla: Jungmann, Alexander, and Bernd Kleinjohann. “Learning Recommendation System for Automated Service Composition.” Proceedings of the 10th IEEE International Conference on Services Computing (SCC), 2013, pp. 97–104, doi:10.1109/SCC.2013.66. short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 10th IEEE International Conference on Services Computing (SCC), 2013, pp. 97–104.' date_created: 2017-10-17T12:42:33Z date_updated: 2022-01-06T07:01:40Z ddc: - '040' doi: 10.1109/SCC.2013.66 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-15T10:43:03Z date_updated: 2018-03-15T10:43:03Z file_id: '1300' file_name: 516-manuscript.pdf file_size: 1762714 relation: main_file success: 1 file_date_updated: 2018-03-15T10:43:03Z has_accepted_license: '1' page: 97-104 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 10th IEEE International Conference on Services Computing (SCC) status: public title: Learning Recommendation System for Automated Service Composition type: conference user_id: '15504' year: '2013' ... --- _id: '568' abstract: - lang: eng text: A major goal of the On-The-Fly Computing project is the automated composition of individual services based on services that are available in dynamic markets. Dependent on the granularity of a market, different alternatives that satisfy the requested functional requirements may emerge. In order to select the best solution, services are usually selected with respect to their quality in terms of inherent non-functional properties. In this paper, we describe our idea of how to model this service selection process as a Markov Decision Process, which we in turn intend to solve by means of Reinforcement Learning techniques in order to control the underlying service composition process. In addition, some initial issues with respect to our approach are addressed. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Kleinjohann B. Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition. In: Proceedings of the 9th IEEE International Conference on Service Computing (SCC). ; 2012:701-702. doi:10.1109/SCC.2012.76' apa: Jungmann, A., & Kleinjohann, B. (2012). Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition. In Proceedings of the 9th IEEE International Conference on Service Computing (SCC) (pp. 701–702). https://doi.org/10.1109/SCC.2012.76 bibtex: '@inproceedings{Jungmann_Kleinjohann_2012, title={Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition}, DOI={10.1109/SCC.2012.76}, booktitle={Proceedings of the 9th IEEE International Conference on Service Computing (SCC)}, author={Jungmann, Alexander and Kleinjohann, Bernd}, year={2012}, pages={701–702} }' chicago: Jungmann, Alexander, and Bernd Kleinjohann. “Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition.” In Proceedings of the 9th IEEE International Conference on Service Computing (SCC), 701–2, 2012. https://doi.org/10.1109/SCC.2012.76. ieee: A. Jungmann and B. Kleinjohann, “Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition,” in Proceedings of the 9th IEEE International Conference on Service Computing (SCC), 2012, pp. 701–702. mla: Jungmann, Alexander, and Bernd Kleinjohann. “Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition.” Proceedings of the 9th IEEE International Conference on Service Computing (SCC), 2012, pp. 701–02, doi:10.1109/SCC.2012.76. short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 9th IEEE International Conference on Service Computing (SCC), 2012, pp. 701–702.' date_created: 2017-10-17T12:42:43Z date_updated: 2022-01-06T07:02:31Z ddc: - '040' doi: 10.1109/SCC.2012.76 file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-15T10:19:51Z date_updated: 2018-03-15T10:19:51Z file_id: '1274' file_name: 568-Towards_the_Application_of_Reinforcement_Learning_Techniques_for_Quality-Based_Service_Selection_in_Automated_Service_Composition.pdf file_size: 122607 relation: main_file success: 1 file_date_updated: 2018-03-15T10:19:51Z has_accepted_license: '1' page: 701-702 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 9th IEEE International Conference on Service Computing (SCC) status: public title: Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition type: conference user_id: '15504' year: '2012' ... --- _id: '571' abstract: - lang: eng text: The paradigm shift from purchasing monolithic software solutions to a dynamic composition of individual solutions entails many new possibilities yet great challenges, too. In order to satisfy user requirements, complex services have to be automatically composed of elementary services. Multiple possibilities of composing a complex service inevitably emerge. The problem of selecting the most appropriate services has to be solved by comparing the different service candidates with respect to their quality in terms of inherent non-functional properties while simultaneously taking the user requirements into account. We are aiming for an integrated service rating and ranking methodology in order to support the automation of the underlying decision-making process. The main contribution of this paper is a first decomposition of the quality-based service selection process, while emphasizing major issues and challenges, which we are addressing in the On-The-Fly Computing project. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann citation: ama: 'Jungmann A, Kleinjohann B. Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition. In: Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION). ; 2012:43-47.' apa: Jungmann, A., & Kleinjohann, B. (2012). Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition. In Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 43–47). bibtex: '@inproceedings{Jungmann_Kleinjohann_2012, title={Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition}, booktitle={Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}, author={Jungmann, Alexander and Kleinjohann, Bernd}, year={2012}, pages={43–47} }' chicago: Jungmann, Alexander, and Bernd Kleinjohann. “Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition.” In Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 43–47, 2012. ieee: A. Jungmann and B. Kleinjohann, “Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition,” in Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2012, pp. 43–47. mla: Jungmann, Alexander, and Bernd Kleinjohann. “Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition.” Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2012, pp. 43–47. short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2012, pp. 43–47.' date_created: 2017-10-17T12:42:43Z date_updated: 2022-01-06T07:02:37Z ddc: - '040' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-15T09:39:02Z date_updated: 2018-03-15T09:39:02Z file_id: '1271' file_name: 571-Towards_an_Integrated_Service_Rating_and_Ranking_Methodology__for_Quality_Based_Service_Selection_in_Automatic_Service_Composition.pdf file_size: 103343 relation: main_file success: 1 file_date_updated: 2018-03-15T09:39:02Z has_accepted_license: '1' page: 43-47 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION) status: public title: Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition type: conference user_id: '15504' year: '2012' ... --- _id: '617' abstract: - lang: eng text: In this paper, a color based feature extraction and classification approach for image processing in embedded systems in presented. The algorithms and data structures developed for this approach pay particular attention to reduce memory consumption and computation power of the entire image processing, since embedded systems usually impose strong restrictions regarding those resources. The feature extraction is realized in terms of an image segmentation algorithm. The criteria of homogeneity for merging pixels and regions is provided by the color classification mechanism, which incorporates appropriate methods for defining, representing and accessing subspaces in the working color space. By doing so, pixels and regions with color values that belong to the same color class can be merged. Furthermore, pixels with redundant color values that do not belong to any pre-defined color class can be completely discarded in order to minimize computational effort. Subsequently, the extracted regions are converted to a more convenient feature representation in terms of statistical moments up to and including second order. For evaluation, the whole image processing approach is applied to a mobile representative of embedded systems within the scope of a simple real-world scenario. author: - first_name: Alexander full_name: Jungmann, Alexander last_name: Jungmann - first_name: Bernd full_name: Kleinjohann, Bernd last_name: Kleinjohann - first_name: Elisabeth full_name: Kleinjohann, Elisabeth id: '15588' last_name: Kleinjohann - first_name: Maarten full_name: Bieshaar, Maarten last_name: Bieshaar citation: ama: 'Jungmann A, Kleinjohann B, Kleinjohann E, Bieshaar M. Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems. In: Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE). ; 2012:22-29.' apa: Jungmann, A., Kleinjohann, B., Kleinjohann, E., & Bieshaar, M. (2012). Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems. In Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE) (pp. 22–29). bibtex: '@inproceedings{Jungmann_Kleinjohann_Kleinjohann_Bieshaar_2012, title={Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems}, booktitle={Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE)}, author={Jungmann, Alexander and Kleinjohann, Bernd and Kleinjohann, Elisabeth and Bieshaar, Maarten}, year={2012}, pages={22–29} }' chicago: Jungmann, Alexander, Bernd Kleinjohann, Elisabeth Kleinjohann, and Maarten Bieshaar. “Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems.” In Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE), 22–29, 2012. ieee: A. Jungmann, B. Kleinjohann, E. Kleinjohann, and M. Bieshaar, “Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems,” in Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE), 2012, pp. 22–29. mla: Jungmann, Alexander, et al. “Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems.” Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE), 2012, pp. 22–29. short: 'A. Jungmann, B. Kleinjohann, E. Kleinjohann, M. Bieshaar, in: Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE), 2012, pp. 22–29.' date_created: 2017-10-17T12:42:52Z date_updated: 2022-01-06T07:02:55Z ddc: - '040' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-15T06:47:50Z date_updated: 2018-03-15T06:47:50Z file_id: '1245' file_name: 617-INTENSIVE2012-Jungmann.pdf file_size: 2787964 relation: main_file success: 1 file_date_updated: 2018-03-15T06:47:50Z has_accepted_license: '1' page: 22-29 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '3' name: SFB 901 - Project Area B publication: Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE) related_material: link: - relation: confirmation url: http://www.thinkmind.org/index.php?view=article&articleid=intensive_2012_1_50_30031 status: public title: Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems type: conference user_id: '477' year: '2012' ...