--- _id: '46335' author: - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: Trautmann H. Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing; 2019. apa: Trautmann, H. (2019). Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing. bibtex: '@book{Trautmann_2019, series={Studies in Classification, Data Analysis, and Knowledge Organization}, title={Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement}, publisher={Springer International Publishing}, author={Trautmann, Heike}, year={2019}, collection={Studies in Classification, Data Analysis, and Knowledge Organization} }' chicago: Trautmann, Heike. Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2019. ieee: H. Trautmann, Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing, 2019. mla: Trautmann, Heike. Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing, 2019. short: H. Trautmann, Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement, Springer International Publishing, 2019. date_created: 2023-08-04T07:43:09Z date_updated: 2023-10-16T13:07:21Z department: - _id: '34' - _id: '819' language: - iso: eng publication_identifier: isbn: - 978-3-030-25147-5 publisher: Springer International Publishing series_title: Studies in Classification, Data Analysis, and Knowledge Organization status: public title: Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement type: book user_id: '15504' year: '2019' ... --- _id: '46346' abstract: - lang: eng text: In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible. author: - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: Kerschke P, Trautmann H. Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation (ECJ). 2019;27(1):99–127. doi:10.1162/evco_a_00236 apa: Kerschke, P., & Trautmann, H. (2019). Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation (ECJ), 27(1), 99–127. https://doi.org/10.1162/evco_a_00236 bibtex: '@article{Kerschke_Trautmann_2019, title={Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning}, volume={27}, DOI={10.1162/evco_a_00236}, number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Trautmann, Heike}, year={2019}, pages={99–127} }' chicago: 'Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.” Evolutionary Computation (ECJ) 27, no. 1 (2019): 99–127. https://doi.org/10.1162/evco_a_00236.' ieee: 'P. Kerschke and H. Trautmann, “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning,” Evolutionary Computation (ECJ), vol. 27, no. 1, pp. 99–127, 2019, doi: 10.1162/evco_a_00236.' mla: Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.” Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 99–127, doi:10.1162/evco_a_00236. short: P. Kerschke, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 99–127. date_created: 2023-08-04T07:51:18Z date_updated: 2023-10-16T13:31:57Z department: - _id: '34' - _id: '819' doi: 10.1162/evco_a_00236 intvolume: ' 27' issue: '1' language: - iso: eng page: 99–127 publication: Evolutionary Computation (ECJ) status: public title: Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning type: journal_article user_id: '15504' volume: 27 year: '2019' ... --- _id: '46347' abstract: - lang: eng text: We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO. author: - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Hao full_name: Wang, Hao last_name: Wang - first_name: Mike full_name: Preuss, Mike last_name: Preuss - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: André full_name: Deutz, André last_name: Deutz - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Michael full_name: Emmerich, Michael last_name: Emmerich citation: ama: Kerschke P, Wang H, Preuss M, et al. Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ). 2019;27(4):577–609. doi:10.1162/evco_a_00234 apa: Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ), 27(4), 577–609. https://doi.org/10.1162/evco_a_00234 bibtex: '@article{Kerschke_Wang_Preuss_Grimme_Deutz_Trautmann_Emmerich_2019, title={Search Dynamics on Multimodal Multi-Objective Problems}, volume={27}, DOI={10.1162/evco_a_00234}, number={4}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann, Heike and Emmerich, Michael}, year={2019}, pages={577–609} }' chicago: 'Kerschke, Pascal, Hao Wang, Mike Preuss, Christian Grimme, André Deutz, Heike Trautmann, and Michael Emmerich. “Search Dynamics on Multimodal Multi-Objective Problems.” Evolutionary Computation (ECJ) 27, no. 4 (2019): 577–609. https://doi.org/10.1162/evco_a_00234.' ieee: 'P. Kerschke et al., “Search Dynamics on Multimodal Multi-Objective Problems,” Evolutionary Computation (ECJ), vol. 27, no. 4, pp. 577–609, 2019, doi: 10.1162/evco_a_00234.' mla: Kerschke, Pascal, et al. “Search Dynamics on Multimodal Multi-Objective Problems.” Evolutionary Computation (ECJ), vol. 27, no. 4, 2019, pp. 577–609, doi:10.1162/evco_a_00234. short: P. Kerschke, H. Wang, M. Preuss, C. Grimme, A. Deutz, H. Trautmann, M. Emmerich, Evolutionary Computation (ECJ) 27 (2019) 577–609. date_created: 2023-08-04T07:52:06Z date_updated: 2023-10-16T13:32:18Z department: - _id: '34' - _id: '819' doi: 10.1162/evco_a_00234 intvolume: ' 27' issue: '4' language: - iso: eng page: 577–609 publication: Evolutionary Computation (ECJ) status: public title: Search Dynamics on Multimodal Multi-Objective Problems type: journal_article user_id: '15504' volume: 27 year: '2019' ... --- _id: '46339' abstract: - lang: eng text: 'Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.' author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Markus full_name: Wagner, Markus last_name: Wagner - first_name: Frank full_name: Neumann, Frank last_name: Neumann - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: Friedrich T, Doerr C, Arnold D, eds. Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV). ; 2019:58–71. doi:10.1145/3299904.3340307' apa: Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In T. Friedrich, C. Doerr, & D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV) (pp. 58–71). https://doi.org/10.1145/3299904.3340307 bibtex: '@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={Potsdam, Germany}, title={Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}, DOI={10.1145/3299904.3340307}, booktitle={Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)}, author={Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, editor={Friedrich, Tobias and Doerr, Carola and Arnold, Dirk}, year={2019}, pages={58–71} }' chicago: Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” In Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich, Carola Doerr, and Dirk Arnold, 58–71. Potsdam, Germany, 2019. https://doi.org/10.1145/3299904.3340307. ieee: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann, “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,” in Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), 2019, pp. 58–71, doi: 10.1145/3299904.3340307.' mla: Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), edited by Tobias Friedrich et al., 2019, pp. 58–71, doi:10.1145/3299904.3340307. short: 'J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann, in: T. Friedrich, C. Doerr, D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 2019, pp. 58–71.' date_created: 2023-08-04T07:45:39Z date_updated: 2024-06-10T11:59:26Z department: - _id: '34' - _id: '819' doi: 10.1145/3299904.3340307 editor: - first_name: Tobias full_name: Friedrich, Tobias last_name: Friedrich - first_name: Carola full_name: Doerr, Carola last_name: Doerr - first_name: Dirk full_name: Arnold, Dirk last_name: Arnold language: - iso: eng page: 58–71 place: Potsdam, Germany publication: Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV) status: public title: Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators type: conference user_id: '15504' year: '2019' ... --- _id: '46338' abstract: - lang: eng text: We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Stephan full_name: Meisel, Stephan last_name: Meisel - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm. In: Deb K, Goodman E, Coello CCA, et al., eds. Evolutionary Multi-Criterion Optimization (EMO). Vol 11411. Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528. doi:10.1007/978-3-030-12598-1_41' apa: 'Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (Vol. 11411, pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41' bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={East Lansing, Michigan, USA}, series={Lecture Notes in Computer Science}, title={Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}, volume={11411}, DOI={10.1007/978-3-030-12598-1_41}, booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={516–528}, collection={Lecture Notes in Computer Science} }' chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” In Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:516–528. Lecture Notes in Computer Science. East Lansing, Michigan, USA: Springer International Publishing, 2019. https://doi.org/10.1007/978-3-030-12598-1_41.' ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm,” in Evolutionary Multi-Criterion Optimization (EMO), 2019, vol. 11411, pp. 516–528, doi: 10.1007/978-3-030-12598-1_41.' mla: 'Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb et al., vol. 11411, Springer International Publishing, 2019, pp. 516–528, doi:10.1007/978-3-030-12598-1_41.' short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E. Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing, East Lansing, Michigan, USA, 2019, pp. 516–528.' date_created: 2023-08-04T07:44:59Z date_updated: 2024-06-10T12:00:05Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-12598-1_41 editor: - first_name: Kalyanmoy full_name: Deb, Kalyanmoy last_name: Deb - first_name: Erik full_name: Goodman, Erik last_name: Goodman - first_name: Coello Carlos A. full_name: Coello, Coello Carlos A. last_name: Coello - first_name: Kathrin full_name: Klamroth, Kathrin last_name: Klamroth - first_name: Kaisa full_name: Miettinen, Kaisa last_name: Miettinen - first_name: Sanaz full_name: Mostaghim, Sanaz last_name: Mostaghim - first_name: Patrick full_name: Reed, Patrick last_name: Reed intvolume: ' 11411' language: - iso: eng page: 516–528 place: East Lansing, Michigan, USA publication: Evolutionary Multi-Criterion Optimization (EMO) publication_identifier: isbn: - 978-3-030-12597-4 publisher: Springer International Publishing series_title: Lecture Notes in Computer Science status: public title: 'Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm' type: conference user_id: '15504' volume: 11411 year: '2019' ... --- _id: '46337' abstract: - lang: eng text: A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos P, eds. Learning and Intelligent Optimization. Vol 11353. Lecture Notes in Computer Science. Springer; 2019:215–219.' apa: 'Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. Pardalos (Eds.), Learning and Intelligent Optimization (Vol. 11353, pp. 215–219). Springer.' bibtex: '@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, volume={11353}, booktitle={Learning and Intelligent Optimization}, publisher={Springer}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} }' chicago: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In Learning and Intelligent Optimization, edited by R Battiti, M Brunato, I Kotsireas, and P Pardalos, 11353:215–219. Lecture Notes in Computer Science. Cham: Springer, 2019.' ieee: 'J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization, 2019, vol. 11353, pp. 215–219.' mla: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” Learning and Intelligent Optimization, edited by R Battiti et al., vol. 11353, Springer, 2019, pp. 215–219.' short: 'J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P. Pardalos (Eds.), Learning and Intelligent Optimization, Springer, Cham, 2019, pp. 215–219.' date_created: 2023-08-04T07:44:10Z date_updated: 2024-06-10T12:00:23Z department: - _id: '34' - _id: '819' editor: - first_name: R full_name: Battiti, R last_name: Battiti - first_name: M full_name: Brunato, M last_name: Brunato - first_name: I full_name: Kotsireas, I last_name: Kotsireas - first_name: P full_name: Pardalos, P last_name: Pardalos intvolume: ' 11353' language: - iso: eng page: 215–219 place: Cham publication: Learning and Intelligent Optimization publication_identifier: isbn: - 978-3-030-05347-5 publisher: Springer series_title: Lecture Notes in Computer Science status: public title: 'Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time' type: conference user_id: '15504' volume: 11353 year: '2019' ... --- _id: '46350' abstract: - lang: eng text: 'The ubiquity of WiFi access points and the sharp increase in WiFi-enabled devices carried by humans have paved the way for WiFi-based indoor positioning and location analysis. Locating people in indoor environments has numerous applications in robotics, crowd control, indoor facility optimization, and automated environment mapping. However, existing WiFi-based positioning systems suffer from two major problems: (1) their accuracy and precision is limited due to inherent noise induced by indoor obstacles, and (2) they only occasionally provide location estimates, namely when a WiFi-equipped device emits a signal. To mitigate these two issues, we propose a novel Gaussian process (GP) model for WiFi signal strength measurements. It allows for simultaneous smoothing (increasing accuracy and precision of estimators) and interpolation (enabling continuous sampling of location estimates). Furthermore, simple and efficient smoothing methods for location estimates are introduced to improve localization performance in real-time settings. Experiments are conducted on two data sets from a large real-world commercial indoor retail environment. Results demonstrate that our approach provides significant improvements in terms of precision and accuracy with respect to unfiltered data. Ultimately, the GP model realizes continuous location sampling with consistently high quality location estimates.' author: - first_name: J.E. full_name: van Engelen, J.E. last_name: van Engelen - first_name: J.J. full_name: van Lier, J.J. last_name: van Lier - first_name: F.W. full_name: Takes, F.W. last_name: Takes - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'van Engelen JE, van Lier JJ, Takes FW, Trautmann H. Accurate WiFi based indoor positioning with continuous location sampling. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD). Springer; 2018:524–540.' apa: van Engelen, J. E., van Lier, J. J., Takes, F. W., & Trautmann, H. (2018). Accurate WiFi based indoor positioning with continuous location sampling. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), 524–540. bibtex: '@inproceedings{van Engelen_van Lier_Takes_Trautmann_2018, place={Dublin, Ireland}, title={Accurate WiFi based indoor positioning with continuous location sampling}, booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)}, publisher={Springer}, author={van Engelen, J.E. and van Lier, J.J. and Takes, F.W. and Trautmann, Heike}, year={2018}, pages={524–540} }' chicago: 'Engelen, J.E. van, J.J. van Lier, F.W. Takes, and Heike Trautmann. “Accurate WiFi Based Indoor Positioning with Continuous Location Sampling.” In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), 524–540. Dublin, Ireland: Springer, 2018.' ieee: J. E. van Engelen, J. J. van Lier, F. W. Takes, and H. Trautmann, “Accurate WiFi based indoor positioning with continuous location sampling,” in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), 2018, pp. 524–540. mla: van Engelen, J. E., et al. “Accurate WiFi Based Indoor Positioning with Continuous Location Sampling.” Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), Springer, 2018, pp. 524–540. short: 'J.E. van Engelen, J.J. van Lier, F.W. Takes, H. Trautmann, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), Springer, Dublin, Ireland, 2018, pp. 524–540.' date_created: 2023-08-04T07:54:43Z date_updated: 2023-10-16T13:33:18Z department: - _id: '34' - _id: '819' language: - iso: eng page: 524–540 place: Dublin, Ireland publication: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD) publisher: Springer status: public title: Accurate WiFi based indoor positioning with continuous location sampling type: conference user_id: '15504' year: '2018' ... --- _id: '46351' abstract: - lang: eng text: Clustering is an important field in data mining that aims to reveal hidden patterns in data sets. It is widely popular in marketing or medical applications and used to identify groups of similar objects. Clustering possibly unbounded and evolving data streams is of particular interest due to the widespread deployment of large and fast data sources such as sensors. The vast majority of stream clustering algorithms employ a two-phase approach where the stream is first summarized in an online phase. Upon request, an offline phase reclusters the aggregations into the final clusters. In this setup, the online component will idle and wait for the next observation in times where the stream is slow. This paper proposes a new stream clustering algorithm called evoStream which performs evolutionary optimization in the idle times of the online phase to incrementally build and refine the final clusters. Since the online phase would idle otherwise, our approach does not reduce the processing speed while effectively removing the computational overhead of the offline phase. In extensive experiments on real data streams we show that the proposed algorithm allows to output clusters of high quality at any time within the stream without the need for additional computational resources. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: Carnein M, Trautmann H. evoStream — Evolutionary Stream Clustering Utilizing Idle Times. Big Data Research. 2018;14:101–111. doi:10.1016/j.bdr.2018.05.005 apa: Carnein, M., & Trautmann, H. (2018). evoStream — Evolutionary Stream Clustering Utilizing Idle Times. Big Data Research, 14, 101–111. https://doi.org/10.1016/j.bdr.2018.05.005 bibtex: '@article{Carnein_Trautmann_2018, title={evoStream — Evolutionary Stream Clustering Utilizing Idle Times}, volume={14}, DOI={10.1016/j.bdr.2018.05.005}, journal={Big Data Research}, author={Carnein, Matthias and Trautmann, Heike}, year={2018}, pages={101–111} }' chicago: 'Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream Clustering Utilizing Idle Times.” Big Data Research 14 (2018): 101–111. https://doi.org/10.1016/j.bdr.2018.05.005.' ieee: 'M. Carnein and H. Trautmann, “evoStream — Evolutionary Stream Clustering Utilizing Idle Times,” Big Data Research, vol. 14, pp. 101–111, 2018, doi: 10.1016/j.bdr.2018.05.005.' mla: Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream Clustering Utilizing Idle Times.” Big Data Research, vol. 14, 2018, pp. 101–111, doi:10.1016/j.bdr.2018.05.005. short: M. Carnein, H. Trautmann, Big Data Research 14 (2018) 101–111. date_created: 2023-08-04T07:55:33Z date_updated: 2023-10-16T13:33:43Z department: - _id: '34' - _id: '819' doi: 10.1016/j.bdr.2018.05.005 intvolume: ' 14' language: - iso: eng page: 101–111 publication: Big Data Research status: public title: evoStream — Evolutionary Stream Clustering Utilizing Idle Times type: journal_article user_id: '15504' volume: 14 year: '2018' ... --- _id: '46353' abstract: - lang: eng text: 'Incorporating decision makers'' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.' author: - first_name: L full_name: Li, L last_name: Li - first_name: Y full_name: Wang, Y last_name: Wang - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: N full_name: Jing, N last_name: Jing - first_name: M full_name: Emmerich, M last_name: Emmerich citation: ama: Li L, Wang Y, Trautmann H, Jing N, Emmerich M. Multiobjective evolutionary algorithms based on target region preferences. Swarm and Evolutionary Computation. 2018;40:196–215. doi:10.1016/j.swevo.2018.02.006 apa: Li, L., Wang, Y., Trautmann, H., Jing, N., & Emmerich, M. (2018). Multiobjective evolutionary algorithms based on target region preferences. Swarm and Evolutionary Computation, 40, 196–215. https://doi.org/10.1016/j.swevo.2018.02.006 bibtex: '@article{Li_Wang_Trautmann_Jing_Emmerich_2018, title={Multiobjective evolutionary algorithms based on target region preferences}, volume={40}, DOI={10.1016/j.swevo.2018.02.006}, journal={Swarm and Evolutionary Computation}, author={Li, L and Wang, Y and Trautmann, Heike and Jing, N and Emmerich, M}, year={2018}, pages={196–215} }' chicago: 'Li, L, Y Wang, Heike Trautmann, N Jing, and M Emmerich. “Multiobjective Evolutionary Algorithms Based on Target Region Preferences.” Swarm and Evolutionary Computation 40 (2018): 196–215. https://doi.org/10.1016/j.swevo.2018.02.006.' ieee: 'L. Li, Y. Wang, H. Trautmann, N. Jing, and M. Emmerich, “Multiobjective evolutionary algorithms based on target region preferences,” Swarm and Evolutionary Computation, vol. 40, pp. 196–215, 2018, doi: 10.1016/j.swevo.2018.02.006.' mla: Li, L., et al. “Multiobjective Evolutionary Algorithms Based on Target Region Preferences.” Swarm and Evolutionary Computation, vol. 40, 2018, pp. 196–215, doi:10.1016/j.swevo.2018.02.006. short: L. Li, Y. Wang, H. Trautmann, N. Jing, M. Emmerich, Swarm and Evolutionary Computation 40 (2018) 196–215. date_created: 2023-08-04T07:56:57Z date_updated: 2023-10-16T13:34:21Z department: - _id: '34' - _id: '819' doi: 10.1016/j.swevo.2018.02.006 intvolume: ' 40' language: - iso: eng page: 196–215 publication: Swarm and Evolutionary Computation status: public title: Multiobjective evolutionary algorithms based on target region preferences type: journal_article user_id: '15504' volume: 40 year: '2018' ... --- _id: '46348' abstract: - lang: eng text: We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Stephan full_name: Meisel, Stephan last_name: Meisel - first_name: Guenter full_name: Rudolph, Guenter last_name: Rudolph - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Local Search Effects in Bi-Objective Orienteering. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’18. ACM; 2018:585–592. doi:10.1145/3205455.3205548' apa: Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548 bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2018, place={New York, NY, USA}, series={GECCO ’18}, title={Local Search Effects in Bi-Objective Orienteering}, DOI={10.1145/3205455.3205548}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={ACM}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Guenter and Trautmann, Heike}, year={2018}, pages={585–592}, collection={GECCO ’18} }' chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Guenter Rudolph, and Heike Trautmann. “Local Search Effects in Bi-Objective Orienteering.” In Proceedings of the Genetic and Evolutionary Computation Conference, 585–592. GECCO ’18. New York, NY, USA: ACM, 2018. https://doi.org/10.1145/3205455.3205548.' ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search Effects in Bi-Objective Orienteering,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2018, pp. 585–592, doi: 10.1145/3205455.3205548.' mla: Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.” Proceedings of the Genetic and Evolutionary Computation Conference, ACM, 2018, pp. 585–592, doi:10.1145/3205455.3205548. short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, ACM, New York, NY, USA, 2018, pp. 585–592.' date_created: 2023-08-04T07:53:16Z date_updated: 2024-06-10T11:59:09Z department: - _id: '34' - _id: '819' doi: 10.1145/3205455.3205548 language: - iso: eng page: 585–592 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-5618-3 publisher: ACM series_title: GECCO ’18 status: public title: Local Search Effects in Bi-Objective Orienteering type: conference user_id: '15504' year: '2018' ... --- _id: '46352' abstract: - lang: eng text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement. author: - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Lars full_name: Kotthoff, Lars last_name: Kotthoff - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Holger H. full_name: Hoos, Holger H. last_name: Hoos - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation (ECJ). 2018;26(4):597–620. doi:10.1162/evco_a_00215 apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation (ECJ), 26(4), 597–620. https://doi.org/10.1162/evco_a_00215 bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215}, number={4}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018}, pages={597–620} }' chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary Computation (ECJ) 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.' ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” Evolutionary Computation (ECJ), vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.' mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary Computation (ECJ), vol. 26, no. 4, 2018, pp. 597–620, doi:10.1162/evco_a_00215. short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary Computation (ECJ) 26 (2018) 597–620. date_created: 2023-08-04T07:56:15Z date_updated: 2024-06-10T11:58:38Z department: - _id: '34' - _id: '819' doi: 10.1162/evco_a_00215 intvolume: ' 26' issue: '4' language: - iso: eng page: 597–620 publication: Evolutionary Computation (ECJ) status: public title: Leveraging TSP Solver Complementarity through Machine Learning type: journal_article user_id: '15504' volume: 26 year: '2018' ... --- _id: '46349' abstract: - lang: eng text: Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms. author: - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion. ; 2018:1737–1744. doi:10.1145/3205651.3208233' apa: 'Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233' bibtex: '@inproceedings{Kerschke_Bossek_Trautmann_2018, place={Kyoto, Japan}, title={Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}, DOI={10.1145/3205651.3208233}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion}, author={Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744} }' chicago: 'Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.” In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, 1737–1744. Kyoto, Japan, 2018. https://doi.org/10.1145/3205651.3208233.' ieee: 'P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, 2018, pp. 1737–1744, doi: 10.1145/3205651.3208233.' mla: 'Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233.' short: 'P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, Kyoto, Japan, 2018, pp. 1737–1744.' date_created: 2023-08-04T07:53:59Z date_updated: 2024-06-10T11:58:54Z department: - _id: '34' - _id: '819' doi: 10.1145/3205651.3208233 language: - iso: eng page: 1737–1744 place: Kyoto, Japan publication: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion publication_identifier: isbn: - 978-1-4503-5764-7/18/07 status: public title: 'Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers' type: conference user_id: '15504' year: '2018' ... --- _id: '46355' abstract: - lang: eng text: "In this chapter we present the adaptions of the recently proposed Directed Search method to the context of unconstrained parameter dependent multi-objective optimization problems (PMOPs). The new method, called \U0001D706-DS, is capable of performing a movement both toward and along the solution set of a given differentiable PMOP. We first discuss the basic variants of the method that use gradient information and describe subsequently modifications that allow for a gradient free realization. Finally, we show that \U0001D706-DS can be used to understand the behavior of stochastic local search within PMOPs to a certain extent which might be interesting for the development of future local search engines, or evolutionary strategies, for the treatment of such problems. We underline all our statements with several numerical results indicating the strength of the novel approach." author: - first_name: Sosa Hernández V full_name: Adrián, Sosa Hernández V last_name: Adrián - first_name: A full_name: Lara, A last_name: Lara - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: G full_name: Rudolph, G last_name: Rudolph - first_name: O full_name: Schütze, O last_name: Schütze citation: ama: 'Adrián SHV, Lara A, Trautmann H, Rudolph G, Schütze O. The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems. In: Schütze O, Trujillo L, Legrand P, Maldonado Y, eds. NEO 15. Springer International Publishing; 2017:281–330. doi:10.1007/978-3-319-44003-3_12' apa: Adrián, S. H. V., Lara, A., Trautmann, H., Rudolph, G., & Schütze, O. (2017). The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems. In O. Schütze, L. Trujillo, P. Legrand, & Y. Maldonado (Eds.), NEO 15 (pp. 281–330). Springer International Publishing. https://doi.org/10.1007/978-3-319-44003-3_12 bibtex: '@inbook{Adrián_Lara_Trautmann_Rudolph_Schütze_2017, place={Cham}, title={The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems}, DOI={10.1007/978-3-319-44003-3_12}, booktitle={NEO 15}, publisher={Springer International Publishing}, author={Adrián, Sosa Hernández V and Lara, A and Trautmann, Heike and Rudolph, G and Schütze, O}, editor={Schütze, O and Trujillo, L and Legrand, P and Maldonado, Y}, year={2017}, pages={281–330} }' chicago: 'Adrián, Sosa Hernández V, A Lara, Heike Trautmann, G Rudolph, and O Schütze. “The Directed Search Method for Unconstrained Parameter Dependent Multi-Objective Optimization Problems.” In NEO 15, edited by O Schütze, L Trujillo, P Legrand, and Y Maldonado, 281–330. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-44003-3_12.' ieee: 'S. H. V. Adrián, A. Lara, H. Trautmann, G. Rudolph, and O. Schütze, “The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems,” in NEO 15, O. Schütze, L. Trujillo, P. Legrand, and Y. Maldonado, Eds. Cham: Springer International Publishing, 2017, pp. 281–330.' mla: Adrián, Sosa Hernández V., et al. “The Directed Search Method for Unconstrained Parameter Dependent Multi-Objective Optimization Problems.” NEO 15, edited by O Schütze et al., Springer International Publishing, 2017, pp. 281–330, doi:10.1007/978-3-319-44003-3_12. short: 'S.H.V. Adrián, A. Lara, H. Trautmann, G. Rudolph, O. Schütze, in: O. Schütze, L. Trujillo, P. Legrand, Y. Maldonado (Eds.), NEO 15, Springer International Publishing, Cham, 2017, pp. 281–330.' date_created: 2023-08-04T15:01:27Z date_updated: 2023-10-16T13:34:49Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-319-44003-3_12 editor: - first_name: O full_name: Schütze, O last_name: Schütze - first_name: L full_name: Trujillo, L last_name: Trujillo - first_name: P full_name: Legrand, P last_name: Legrand - first_name: Y full_name: Maldonado, Y last_name: Maldonado language: - iso: eng page: 281–330 place: Cham publication: NEO 15 publication_identifier: isbn: - 978-3-319-44003-3 publisher: Springer International Publishing status: public title: The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems type: book_chapter user_id: '15504' year: '2017' ... --- _id: '46360' abstract: - lang: eng text: Nowadays customers expect a seamless interaction with companies throughout all available communication channels. However, many companies rely on different software solutions to handle each channel, which leads to heterogeneous IT infrastructures and isolated data sources. Omni-Channel CRM is a holistic approach towards a unified view on the customer across all channels. This paper introduces three case studies which demonstrate challenges of omni-channel CRM and the value it can provide. The first case study shows how to integrate and visualise data from different sources which can support operational and strategic decision. In the second case study, a social media analysis approach is discussed which provides benefits by offering reports of service performance across channels. The third case study applies customer segmentation to an online fashion retailer in order to identify customer profiles. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Markus full_name: Heuchert, Markus last_name: Heuchert - first_name: Leschek full_name: Homann, Leschek last_name: Homann - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Gottfried full_name: Vossen, Gottfried last_name: Vossen - first_name: Jörg full_name: Becker, Jörg last_name: Becker - first_name: Karsten full_name: Kraume, Karsten last_name: Kraume citation: ama: 'Carnein M, Heuchert M, Homann L, et al. Towards Efficient and Informative Omni-Channel Customer Relationship Management. In: de Cesare S, Ulrich F, eds. Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17). Vol 10651. Lecture Notes in Computer Science. Springer International Publishing; 2017:69–78. doi:10.1007/978-3-319-70625-2_7' apa: Carnein, M., Heuchert, M., Homann, L., Trautmann, H., Vossen, G., Becker, J., & Kraume, K. (2017). Towards Efficient and Informative Omni-Channel Customer Relationship Management. In S. de Cesare & F. Ulrich (Eds.), Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17) (Vol. 10651, pp. 69–78). Springer International Publishing. https://doi.org/10.1007/978-3-319-70625-2_7 bibtex: '@inproceedings{Carnein_Heuchert_Homann_Trautmann_Vossen_Becker_Kraume_2017, place={Valencia, Spain}, series={Lecture Notes in Computer Science}, title={Towards Efficient and Informative Omni-Channel Customer Relationship Management}, volume={10651}, DOI={10.1007/978-3-319-70625-2_7}, booktitle={Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17)}, publisher={Springer International Publishing}, author={Carnein, Matthias and Heuchert, Markus and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried and Becker, Jörg and Kraume, Karsten}, editor={de Cesare, Sergio and Ulrich, Frank}, year={2017}, pages={69–78}, collection={Lecture Notes in Computer Science} }' chicago: 'Carnein, Matthias, Markus Heuchert, Leschek Homann, Heike Trautmann, Gottfried Vossen, Jörg Becker, and Karsten Kraume. “Towards Efficient and Informative Omni-Channel Customer Relationship Management.” In Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), edited by Sergio de Cesare and Frank Ulrich, 10651:69–78. Lecture Notes in Computer Science. Valencia, Spain: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-70625-2_7.' ieee: 'M. Carnein et al., “Towards Efficient and Informative Omni-Channel Customer Relationship Management,” in Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), 2017, vol. 10651, pp. 69–78, doi: 10.1007/978-3-319-70625-2_7.' mla: Carnein, Matthias, et al. “Towards Efficient and Informative Omni-Channel Customer Relationship Management.” Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), edited by Sergio de Cesare and Frank Ulrich, vol. 10651, Springer International Publishing, 2017, pp. 69–78, doi:10.1007/978-3-319-70625-2_7. short: 'M. Carnein, M. Heuchert, L. Homann, H. Trautmann, G. Vossen, J. Becker, K. Kraume, in: S. de Cesare, F. Ulrich (Eds.), Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), Springer International Publishing, Valencia, Spain, 2017, pp. 69–78.' date_created: 2023-08-04T15:05:43Z date_updated: 2023-10-16T13:36:40Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-319-70625-2_7 editor: - first_name: Sergio full_name: de Cesare, Sergio last_name: de Cesare - first_name: Frank full_name: Ulrich, Frank last_name: Ulrich intvolume: ' 10651' language: - iso: eng page: 69–78 place: Valencia, Spain publication: Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17) publication_identifier: isbn: - 978-3-319-70625-2 publisher: Springer International Publishing series_title: Lecture Notes in Computer Science status: public title: Towards Efficient and Informative Omni-Channel Customer Relationship Management type: conference user_id: '15504' volume: 10651 year: '2017' ... --- _id: '46361' abstract: - lang: eng text: Until recently, customer service was exclusively provided over traditional channels. Cus- tomers could write an email or call a service center if they had questions or problems with a product or service. In recent times, this has changed dramatically as companies explore new channels to offer customer service. With the increasing popularity of social media, more companies thrive to provide customer service also over Facebook and Twitter. Companies aim to provide a better customer ex- perience by offering more convenient channels to contact a company. In addition, this unburdens traditional channels which are costly to maintain. This paper empirically evaluates the performance of customer service in social media by analysing a multitude of companies in the airline industry. We have collected several million customer service requests from Twitter and Facebook and auto- matically analyzed how efficient the service strategies of the respective companies are in terms of response rate and time. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Leschek full_name: Homann, Leschek last_name: Homann - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Gottfried full_name: Vossen, Gottfried last_name: Vossen - first_name: Karsten full_name: Kraume, Karsten last_name: Kraume citation: ama: 'Carnein M, Homann L, Trautmann H, Vossen G, Kraume K. Customer Service in Social Media — An Empirical Study of the Airline Industry. In: Ritter N, Schwarz H, Klettke M, Thor A, Kopp O, Bernhard MW, eds. Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17). Vol P-266. Lecture Notes in Informatics (LNI). Gesellschaft für Informatik; 2017:33–40.' apa: 'Carnein, M., Homann, L., Trautmann, H., Vossen, G., & Kraume, K. (2017). Customer Service in Social Media — An Empirical Study of the Airline Industry. In N. Ritter, H. Schwarz, M. Klettke, A. Thor, O. Kopp, & M. W. Bernhard (Eds.), Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17): Vol. P-266 (pp. 33–40). Gesellschaft für Informatik.' bibtex: '@inproceedings{Carnein_Homann_Trautmann_Vossen_Kraume_2017, place={Stuttgart, Germany}, series={Lecture Notes in Informatics (LNI)}, title={Customer Service in Social Media — An Empirical Study of the Airline Industry}, volume={P-266}, booktitle={Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17)}, publisher={Gesellschaft für Informatik}, author={Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried and Kraume, Karsten}, editor={Ritter, Norbert and Schwarz, Holger and Klettke, Meike and Thor, Andreas and Kopp, Oliver and Bernhard, Matthias Wieland}, year={2017}, pages={33–40}, collection={Lecture Notes in Informatics (LNI)} }' chicago: 'Carnein, Matthias, Leschek Homann, Heike Trautmann, Gottfried Vossen, and Karsten Kraume. “Customer Service in Social Media — An Empirical Study of the Airline Industry.” In Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17), edited by Norbert Ritter, Holger Schwarz, Meike Klettke, Andreas Thor, Oliver Kopp, and Matthias Wieland Bernhard, P-266:33–40. Lecture Notes in Informatics (LNI). Stuttgart, Germany: Gesellschaft für Informatik, 2017.' ieee: M. Carnein, L. Homann, H. Trautmann, G. Vossen, and K. Kraume, “Customer Service in Social Media — An Empirical Study of the Airline Industry,” in Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17), 2017, vol. P-266, pp. 33–40. mla: Carnein, Matthias, et al. “Customer Service in Social Media — An Empirical Study of the Airline Industry.” Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17), edited by Norbert Ritter et al., vol. P-266, Gesellschaft für Informatik, 2017, pp. 33–40. short: 'M. Carnein, L. Homann, H. Trautmann, G. Vossen, K. Kraume, in: N. Ritter, H. Schwarz, M. Klettke, A. Thor, O. Kopp, M.W. Bernhard (Eds.), Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17), Gesellschaft für Informatik, Stuttgart, Germany, 2017, pp. 33–40.' date_created: 2023-08-04T15:06:41Z date_updated: 2023-10-16T13:36:58Z department: - _id: '34' - _id: '819' editor: - first_name: Norbert full_name: Ritter, Norbert last_name: Ritter - first_name: Holger full_name: Schwarz, Holger last_name: Schwarz - first_name: Meike full_name: Klettke, Meike last_name: Klettke - first_name: Andreas full_name: Thor, Andreas last_name: Thor - first_name: Oliver full_name: Kopp, Oliver last_name: Kopp - first_name: Matthias Wieland full_name: Bernhard, Matthias Wieland last_name: Bernhard language: - iso: eng page: 33–40 place: Stuttgart, Germany publication: Proceedings of the 17$^th$ Conference on Database Systems for Business, Technology, and Web (BTW ’17) publication_identifier: issn: - 978-3-88579-660-2 publisher: Gesellschaft für Informatik series_title: Lecture Notes in Informatics (LNI) status: public title: Customer Service in Social Media — An Empirical Study of the Airline Industry type: conference user_id: '15504' volume: P-266 year: '2017' ... --- _id: '46356' abstract: - lang: eng text: Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preference-based multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Protégé. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities. author: - first_name: L full_name: Li, L last_name: Li - first_name: I full_name: Yevseyeva, I last_name: Yevseyeva - first_name: V full_name: Basto-Fernandes, V last_name: Basto-Fernandes - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: N full_name: Jing, N last_name: Jing - first_name: M full_name: Emmerich, M last_name: Emmerich citation: ama: 'Li L, Yevseyeva I, Basto-Fernandes V, Trautmann H, Jing N, Emmerich M. Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms. In: Trautmann H, Rudolph G, Klamroth K, et al., eds. Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. Springer International Publishing; 2017:406–421. doi:10.1007/978-3-319-54157-0_28' apa: 'Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N., & Emmerich, M. (2017). Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, & C. Grimme (Eds.), Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings (pp. 406–421). Springer International Publishing. https://doi.org/10.1007/978-3-319-54157-0_28' bibtex: '@inbook{Li_Yevseyeva_Basto-Fernandes_Trautmann_Jing_Emmerich_2017, place={Cham}, title={Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms}, DOI={10.1007/978-3-319-54157-0_28}, booktitle={Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings}, publisher={Springer International Publishing}, author={Li, L and Yevseyeva, I and Basto-Fernandes, V and Trautmann, Heike and Jing, N and Emmerich, M}, editor={Trautmann, H and Rudolph, G and Klamroth, K and Schütze, O and Wiecek, M and Jin, Y and Grimme, C}, year={2017}, pages={406–421} }' chicago: 'Li, L, I Yevseyeva, V Basto-Fernandes, Heike Trautmann, N Jing, and M Emmerich. “Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms.” In Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited by H Trautmann, G Rudolph, K Klamroth, O Schütze, M Wiecek, Y Jin, and C Grimme, 406–421. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-54157-0_28.' ieee: 'L. Li, I. Yevseyeva, V. Basto-Fernandes, H. Trautmann, N. Jing, and M. Emmerich, “Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms,” in Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, and C. Grimme, Eds. Cham: Springer International Publishing, 2017, pp. 406–421.' mla: 'Li, L., et al. “Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms.” Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited by H Trautmann et al., Springer International Publishing, 2017, pp. 406–421, doi:10.1007/978-3-319-54157-0_28.' short: 'L. Li, I. Yevseyeva, V. Basto-Fernandes, H. Trautmann, N. Jing, M. Emmerich, in: H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme (Eds.), Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, Springer International Publishing, Cham, 2017, pp. 406–421.' date_created: 2023-08-04T15:02:20Z date_updated: 2023-10-16T13:35:17Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-319-54157-0_28 editor: - first_name: H full_name: Trautmann, H last_name: Trautmann - first_name: G full_name: Rudolph, G last_name: Rudolph - first_name: K full_name: Klamroth, K last_name: Klamroth - first_name: O full_name: Schütze, O last_name: Schütze - first_name: M full_name: Wiecek, M last_name: Wiecek - first_name: Y full_name: Jin, Y last_name: Jin - first_name: C full_name: Grimme, C last_name: Grimme language: - iso: eng page: 406–421 place: Cham publication: 'Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings' publication_identifier: isbn: - 978-3-319-54157-0 publisher: Springer International Publishing status: public title: Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms type: book_chapter user_id: '15504' year: '2017' ... --- _id: '46357' abstract: - lang: eng text: The liner shipping fleet repositioning problem (LSFRP) is a central optimization problem within the container shipping industry. Several approaches exist for solving this problem using exact and heuristic techniques, however all of them use a single objective function for determining an optimal solution. We propose a multi-objective approach based on a simulated annealing heuristic so that repositioning coordinators can better balance profit making with cost-savings and environmental sustainability. As the first multi-objective approach in the area of liner shipping routing, we show that giving more options to decision makers need not be costly. Indeed, our approach requires no extra runtime than a weighted objective heuristic and provides a rich set of solutions along the Pareto front. author: - first_name: K full_name: Tierney, K last_name: Tierney - first_name: J full_name: Handali, J last_name: Handali - first_name: C full_name: Grimme, C last_name: Grimme - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Tierney K, Handali J, Grimme C, Trautmann H. Multi-objective Optimization for Liner Shipping Fleet Repositioning. In: Trautmann H, Rudolph G, Klamroth K, et al., eds. Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. Springer International Publishing; 2017:622–638. doi:10.1007/978-3-319-54157-0_42' apa: 'Tierney, K., Handali, J., Grimme, C., & Trautmann, H. (2017). Multi-objective Optimization for Liner Shipping Fleet Repositioning. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, & C. Grimme (Eds.), Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings (pp. 622–638). Springer International Publishing. https://doi.org/10.1007/978-3-319-54157-0_42' bibtex: '@inbook{Tierney_Handali_Grimme_Trautmann_2017, place={Cham}, title={Multi-objective Optimization for Liner Shipping Fleet Repositioning}, DOI={10.1007/978-3-319-54157-0_42}, booktitle={Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings}, publisher={Springer International Publishing}, author={Tierney, K and Handali, J and Grimme, C and Trautmann, Heike}, editor={Trautmann, H and Rudolph, G and Klamroth, K and Schütze, O and Wiecek, M and Jin, Y and Grimme, C}, year={2017}, pages={622–638} }' chicago: 'Tierney, K, J Handali, C Grimme, and Heike Trautmann. “Multi-Objective Optimization for Liner Shipping Fleet Repositioning.” In Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited by H Trautmann, G Rudolph, K Klamroth, O Schütze, M Wiecek, Y Jin, and C Grimme, 622–638. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-54157-0_42.' ieee: 'K. Tierney, J. Handali, C. Grimme, and H. Trautmann, “Multi-objective Optimization for Liner Shipping Fleet Repositioning,” in Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, and C. Grimme, Eds. Cham: Springer International Publishing, 2017, pp. 622–638.' mla: 'Tierney, K., et al. “Multi-Objective Optimization for Liner Shipping Fleet Repositioning.” Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited by H Trautmann et al., Springer International Publishing, 2017, pp. 622–638, doi:10.1007/978-3-319-54157-0_42.' short: 'K. Tierney, J. Handali, C. Grimme, H. Trautmann, in: H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme (Eds.), Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, Springer International Publishing, Cham, 2017, pp. 622–638.' date_created: 2023-08-04T15:03:17Z date_updated: 2023-10-16T13:35:41Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-319-54157-0_42 editor: - first_name: H full_name: Trautmann, H last_name: Trautmann - first_name: G full_name: Rudolph, G last_name: Rudolph - first_name: K full_name: Klamroth, K last_name: Klamroth - first_name: O full_name: Schütze, O last_name: Schütze - first_name: M full_name: Wiecek, M last_name: Wiecek - first_name: Y full_name: Jin, Y last_name: Jin - first_name: C full_name: Grimme, C last_name: Grimme language: - iso: eng page: 622–638 place: Cham publication: 'Evolutionary Multi-Criterion Optimization: 9$^th$ International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings' publication_identifier: isbn: - 978-3-319-54157-0 publisher: Springer International Publishing status: public title: Multi-objective Optimization for Liner Shipping Fleet Repositioning type: book_chapter user_id: '15504' year: '2017' ... --- _id: '46359' abstract: - lang: eng text: This paper proposes a new stream clustering algorithm for text streams. The algorithm combines concepts from stream clustering and text analysis in order to incrementally maintain a number of text droplets that represent topics within the stream. Our algorithm adapts to changes of topic over time and can handle noise and outliers gracefully by decaying the importance of irrelevant clusters. We demonstrate the performance of our approach by using more than one million real-world texts from the video streaming platform Twitch.tv. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Carnein M, Assenmacher D, Trautmann H. Stream Clustering of Chat Messages with Applications to Twitch Streams. In: de Cesare S, Ulrich F, eds. Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17). Springer International Publishing; 2017:79–88. doi:10.1007/978-3-319-70625-2_8' apa: Carnein, M., Assenmacher, D., & Trautmann, H. (2017). Stream Clustering of Chat Messages with Applications to Twitch Streams. In S. de Cesare & F. Ulrich (Eds.), Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17) (pp. 79–88). Springer International Publishing. https://doi.org/10.1007/978-3-319-70625-2_8 bibtex: '@inproceedings{Carnein_Assenmacher_Trautmann_2017, place={Valencia, Spain}, title={Stream Clustering of Chat Messages with Applications to Twitch Streams}, DOI={10.1007/978-3-319-70625-2_8}, booktitle={Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17)}, publisher={Springer International Publishing}, author={Carnein, Matthias and Assenmacher, Dennis and Trautmann, Heike}, editor={de Cesare, Sergio and Ulrich, Frank}, year={2017}, pages={79–88} }' chicago: 'Carnein, Matthias, Dennis Assenmacher, and Heike Trautmann. “Stream Clustering of Chat Messages with Applications to Twitch Streams.” In Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), edited by Sergio de Cesare and Frank Ulrich, 79–88. Valencia, Spain: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-70625-2_8.' ieee: 'M. Carnein, D. Assenmacher, and H. Trautmann, “Stream Clustering of Chat Messages with Applications to Twitch Streams,” in Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), 2017, pp. 79–88, doi: 10.1007/978-3-319-70625-2_8.' mla: Carnein, Matthias, et al. “Stream Clustering of Chat Messages with Applications to Twitch Streams.” Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), edited by Sergio de Cesare and Frank Ulrich, Springer International Publishing, 2017, pp. 79–88, doi:10.1007/978-3-319-70625-2_8. short: 'M. Carnein, D. Assenmacher, H. Trautmann, in: S. de Cesare, F. Ulrich (Eds.), Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17), Springer International Publishing, Valencia, Spain, 2017, pp. 79–88.' date_created: 2023-08-04T15:04:57Z date_updated: 2023-10-16T13:36:23Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-319-70625-2_8 editor: - first_name: Sergio full_name: de Cesare, Sergio last_name: de Cesare - first_name: Frank full_name: Ulrich, Frank last_name: Ulrich language: - iso: eng page: 79–88 place: Valencia, Spain publication: Proceedings of the 36$^th$ International Conference on Conceptual Modeling (ER’17) publication_identifier: isbn: - 978-3-319-70625-2 publisher: Springer International Publishing status: public title: Stream Clustering of Chat Messages with Applications to Twitch Streams type: conference user_id: '15504' year: '2017' ... --- _id: '46362' abstract: - lang: eng text: Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media, and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term social bot is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to realize meaningful interactions with other humans. This finally leads to the conclusion that hybridization is a challenge for current detection mechanisms and has to be handled with more sophisticated approaches to identify political propaganda distributed with social bots. author: - first_name: C full_name: Grimme, C last_name: Grimme - first_name: M full_name: Preuss, M last_name: Preuss - first_name: L full_name: Adam, L last_name: Adam - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Grimme C, Preuss M, Adam L, Trautmann H. Social Bots: Human-Like by Means of Human Control? Big Data. 2017;5(4):279–293. doi:10.1089/big.2017.0044' apa: 'Grimme, C., Preuss, M., Adam, L., & Trautmann, H. (2017). Social Bots: Human-Like by Means of Human Control? Big Data, 5(4), 279–293. https://doi.org/10.1089/big.2017.0044' bibtex: '@article{Grimme_Preuss_Adam_Trautmann_2017, title={Social Bots: Human-Like by Means of Human Control?}, volume={5}, DOI={10.1089/big.2017.0044}, number={4}, journal={Big Data}, author={Grimme, C and Preuss, M and Adam, L and Trautmann, Heike}, year={2017}, pages={279–293} }' chicago: 'Grimme, C, M Preuss, L Adam, and Heike Trautmann. “Social Bots: Human-Like by Means of Human Control?” Big Data 5, no. 4 (2017): 279–293. https://doi.org/10.1089/big.2017.0044.' ieee: 'C. Grimme, M. Preuss, L. Adam, and H. Trautmann, “Social Bots: Human-Like by Means of Human Control?,” Big Data, vol. 5, no. 4, pp. 279–293, 2017, doi: 10.1089/big.2017.0044.' mla: 'Grimme, C., et al. “Social Bots: Human-Like by Means of Human Control?” Big Data, vol. 5, no. 4, 2017, pp. 279–293, doi:10.1089/big.2017.0044.' short: C. Grimme, M. Preuss, L. Adam, H. Trautmann, Big Data 5 (2017) 279–293. date_created: 2023-08-04T15:07:56Z date_updated: 2023-10-16T13:37:14Z department: - _id: '34' - _id: '819' doi: 10.1089/big.2017.0044 intvolume: ' 5' issue: '4' language: - iso: eng page: 279–293 publication: Big Data status: public title: 'Social Bots: Human-Like by Means of Human Control?' type: journal_article user_id: '15504' volume: 5 year: '2017' ... --- _id: '46358' abstract: - lang: eng text: Analysing streaming data has received considerable attention over the recent years. A key research area in this field is stream clustering which aims to recognize patterns in a possibly unbounded data stream of varying speed and structure. Over the past decades a multitude of new stream clustering algorithms have been proposed. However, to the best of our knowledge, no rigorous analysis and comparison of the different approaches has been performed. Our paper fills this gap and provides extensive experiments for a total of ten popular algorithms. We utilize a number of standard data sets of both, real and synthetic data and identify key weaknesses and strengths of the existing algorithms. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Carnein M, Assenmacher D, Trautmann H. An Empirical Comparison of Stream Clustering Algorithms. In: Proceedings of the ACM International Conference on Computing Frontiers (CF ’17). ; 2017:361–365. doi:10.1145/3075564.3078887' apa: Carnein, M., Assenmacher, D., & Trautmann, H. (2017). An Empirical Comparison of Stream Clustering Algorithms. Proceedings of the ACM International Conference on Computing Frontiers (CF ’17), 361–365. https://doi.org/10.1145/3075564.3078887 bibtex: '@inproceedings{Carnein_Assenmacher_Trautmann_2017, place={Siena, Italy}, title={An Empirical Comparison of Stream Clustering Algorithms}, DOI={10.1145/3075564.3078887}, booktitle={Proceedings of the ACM International Conference on Computing Frontiers (CF ’17)}, author={Carnein, Matthias and Assenmacher, Dennis and Trautmann, Heike}, year={2017}, pages={361–365} }' chicago: Carnein, Matthias, Dennis Assenmacher, and Heike Trautmann. “An Empirical Comparison of Stream Clustering Algorithms.” In Proceedings of the ACM International Conference on Computing Frontiers (CF ’17), 361–365. Siena, Italy, 2017. https://doi.org/10.1145/3075564.3078887. ieee: 'M. Carnein, D. Assenmacher, and H. Trautmann, “An Empirical Comparison of Stream Clustering Algorithms,” in Proceedings of the ACM International Conference on Computing Frontiers (CF ’17), 2017, pp. 361–365, doi: 10.1145/3075564.3078887.' mla: Carnein, Matthias, et al. “An Empirical Comparison of Stream Clustering Algorithms.” Proceedings of the ACM International Conference on Computing Frontiers (CF ’17), 2017, pp. 361–365, doi:10.1145/3075564.3078887. short: 'M. Carnein, D. Assenmacher, H. Trautmann, in: Proceedings of the ACM International Conference on Computing Frontiers (CF ’17), Siena, Italy, 2017, pp. 361–365.' date_created: 2023-08-04T15:04:09Z date_updated: 2023-10-16T13:35:59Z department: - _id: '34' - _id: '819' doi: 10.1145/3075564.3078887 language: - iso: eng page: 361–365 place: Siena, Italy publication: Proceedings of the ACM International Conference on Computing Frontiers (CF ’17) publication_identifier: isbn: - 978-1-4503-4487-6/17/05 status: public title: An Empirical Comparison of Stream Clustering Algorithms type: conference user_id: '15504' year: '2017' ...