--- _id: '8312' author: - first_name: Frederik Simon full_name: Bäumer, Frederik Simon id: '38837' last_name: Bäumer - first_name: Michaela full_name: Geierhos, Michaela id: '42496' last_name: Geierhos orcid: 0000-0002-8180-5606 citation: ama: 'Bäumer FS, Geierhos M. Requirements Engineering in OTF-Computing. In: Encyclopedia.Pub. Basel, Switzerland: MDPI; 2019.' apa: 'Bäumer, F. S., & Geierhos, M. (2019). Requirements Engineering in OTF-Computing. In encyclopedia.pub. Basel, Switzerland: MDPI.' bibtex: '@inbook{Bäumer_Geierhos_2019, place={Basel, Switzerland}, title={Requirements Engineering in OTF-Computing}, booktitle={encyclopedia.pub}, publisher={MDPI}, author={Bäumer, Frederik Simon and Geierhos, Michaela}, year={2019} }' chicago: 'Bäumer, Frederik Simon, and Michaela Geierhos. “Requirements Engineering in OTF-Computing.” In Encyclopedia.Pub. Basel, Switzerland: MDPI, 2019.' ieee: 'F. S. Bäumer and M. Geierhos, “Requirements Engineering in OTF-Computing,” in encyclopedia.pub, Basel, Switzerland: MDPI, 2019.' mla: Bäumer, Frederik Simon, and Michaela Geierhos. “Requirements Engineering in OTF-Computing.” Encyclopedia.Pub, MDPI, 2019. short: 'F.S. Bäumer, M. Geierhos, in: Encyclopedia.Pub, MDPI, Basel, Switzerland, 2019.' date_created: 2019-03-05T08:54:37Z date_updated: 2022-01-06T07:03:53Z department: - _id: '36' - _id: '1' - _id: '579' keyword: - OTF Computing - Natural Language Processing - Requirements Engineering language: - iso: eng main_file_link: - open_access: '1' url: https://encyclopedia.pub/131 oa: '1' place: Basel, Switzerland project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '9' name: SFB 901 - Subproject B1 publication: encyclopedia.pub publication_status: published publisher: MDPI quality_controlled: '1' status: public title: Requirements Engineering in OTF-Computing type: encyclopedia_article user_id: '42496' year: '2019' ... --- _id: '2332' abstract: - lang: ger text: "Ein wichtiges Element der Digitalen Transformation ist die Digitalisierung der Prozesse in Unternehmen. Eine Herausforderung besteht hierbei in der systematischen Erkennung von Digitalisierungspotenzialen in Prozessen. Bestehende Ansätze benötigen Experten, welche Potenziale über ihre Erfahrung oder zeitaufwendig mithilfe von Musterkatalogen identifizieren.\r\nIn diesem Artikel werden verschiedene Digitalisierungspotenziale klassifiziert und Muster für ein zukünftiges musterbasiertes Analyseverfahren zur automatisierten Identifikation von Digitalisierungspotenzialen in BPMN-Diagrammen beschrieben. Im Vergleich zu bestehenden Ansätzen erlaubt es Experten die Identifizierung von Digitalisierungspotenzialen effizienter und effektiver durchzuführen." - lang: eng text: "An important element of digital transformation is the digitalization of processes within enterprises.\r\nA major challenge is the systematic identification of digitalization potentials in business processes.\r\nExisting approaches require experts who identify said potentials by using the time-consuming method of pattern catalogs or by relying on their professional experiences. \r\nIn this paper we classify potentials of digitalization and corresponding patterns for a future pattern-based analysis procedure. This shall allow for the automated identification of digitalization potentials in BPMN diagrams. In comparison to existing approaches, our proposed method could support a more efficient and effective identification of digitalization potentials by experts." author: - first_name: Florian full_name: Rittmeier, Florian id: '5281' last_name: Rittmeier - first_name: Gregor full_name: Engels, Gregor id: '107' last_name: Engels - first_name: Alexander full_name: Teetz, Alexander id: '5319' last_name: Teetz citation: ama: 'Rittmeier F, Engels G, Teetz A. Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes). In: Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018. Vol 2060. CEUR Workshop Proceedings. CEUR-WS.org; 2018:215--221.' apa: Rittmeier, F., Engels, G., & Teetz, A. (2018). Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes). In Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018. (Vol. 2060, pp. 215--221). CEUR-WS.org. bibtex: '@inproceedings{Rittmeier_Engels_Teetz_2018, series={CEUR Workshop Proceedings}, title={Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes)}, volume={2060}, booktitle={Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018.}, publisher={CEUR-WS.org}, author={Rittmeier, Florian and Engels, Gregor and Teetz, Alexander}, year={2018}, pages={215--221}, collection={CEUR Workshop Proceedings} }' chicago: Rittmeier, Florian, Gregor Engels, and Alexander Teetz. “Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes).” In Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018., 2060:215--221. CEUR Workshop Proceedings. CEUR-WS.org, 2018. ieee: F. Rittmeier, G. Engels, and A. Teetz, “Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes),” in Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018., 2018, vol. 2060, pp. 215--221. mla: Rittmeier, Florian, et al. “Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes).” Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018., vol. 2060, CEUR-WS.org, 2018, pp. 215--221. short: 'F. Rittmeier, G. Engels, A. Teetz, in: Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018., CEUR-WS.org, 2018, pp. 215--221.' date_created: 2018-04-13T09:44:09Z date_updated: 2022-01-06T06:55:49Z department: - _id: '66' intvolume: ' 2060' keyword: - Digitalisierungspotenziale - BPI - Digitale Transformation - Information Flow-Modellierung - Patterns - Requirements Engineering language: - iso: ger main_file_link: - open_access: '1' url: http://ceur-ws.org/Vol-2060/rebpm2.pdf oa: '1' page: 215--221 publication: Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018. publication_status: published publisher: CEUR-WS.org series_title: CEUR Workshop Proceedings status: public title: Digitalisierungspotenziale in Geschäftsprozessen effizient und effektiv erkennen (Effective and Efficient Identification of Digitalization Potentials in Business Processes) type: conference user_id: '5281' volume: 2060 year: '2018' ... --- _id: '97' abstract: - lang: eng text: Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets. author: - first_name: Lorijn full_name: van Rooijen, Lorijn id: '58843' last_name: van Rooijen - first_name: Frederik Simon full_name: Bäumer, Frederik Simon id: '38837' last_name: Bäumer - first_name: Marie Christin full_name: Platenius, Marie Christin last_name: Platenius - first_name: Michaela full_name: Geierhos, Michaela id: '42496' last_name: Geierhos orcid: 0000-0002-8180-5606 - first_name: Heiko full_name: Hamann, Heiko last_name: Hamann - first_name: Gregor full_name: Engels, Gregor id: '107' last_name: Engels citation: ama: 'van Rooijen L, Bäumer FS, Platenius MC, Geierhos M, Hamann H, Engels G. From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process. In: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW). Piscataway, NJ, USA: IEEE; 2017:379-385. doi:10.1109/REW.2017.26' apa: 'van Rooijen, L., Bäumer, F. S., Platenius, M. C., Geierhos, M., Hamann, H., & Engels, G. (2017). From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process. In 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW) (pp. 379–385). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/REW.2017.26' bibtex: '@inproceedings{van Rooijen_Bäumer_Platenius_Geierhos_Hamann_Engels_2017, place={Piscataway, NJ, USA}, title={From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process}, DOI={10.1109/REW.2017.26}, booktitle={2017 IEEE 25th International Requirements Engineering Conference Workshops (REW)}, publisher={IEEE}, author={van Rooijen, Lorijn and Bäumer, Frederik Simon and Platenius, Marie Christin and Geierhos, Michaela and Hamann, Heiko and Engels, Gregor}, year={2017}, pages={379–385} }' chicago: 'Rooijen, Lorijn van, Frederik Simon Bäumer, Marie Christin Platenius, Michaela Geierhos, Heiko Hamann, and Gregor Engels. “From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process.” In 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW), 379–85. Piscataway, NJ, USA: IEEE, 2017. https://doi.org/10.1109/REW.2017.26.' ieee: 'L. van Rooijen, F. S. Bäumer, M. C. Platenius, M. Geierhos, H. Hamann, and G. Engels, “From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process,” in 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW), Lisbon, Portugal, 2017, pp. 379–385.' mla: 'van Rooijen, Lorijn, et al. “From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process.” 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW), IEEE, 2017, pp. 379–85, doi:10.1109/REW.2017.26.' short: 'L. van Rooijen, F.S. Bäumer, M.C. Platenius, M. Geierhos, H. Hamann, G. Engels, in: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW), IEEE, Piscataway, NJ, USA, 2017, pp. 379–385.' conference: end_date: 2017-09-08 location: Lisbon, Portugal name: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW) start_date: 2017-09-04 date_created: 2017-10-17T12:41:10Z date_updated: 2022-01-06T07:04:18Z ddc: - '000' department: - _id: '36' - _id: '1' - _id: '579' - _id: '34' - _id: '7' - _id: '66' - _id: '238' - _id: '63' doi: 10.1109/REW.2017.26 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T14:50:35Z date_updated: 2018-11-02T14:50:35Z file_id: '5285' file_name: 08054881.pdf file_size: 433613 relation: main_file success: 1 file_date_updated: 2018-11-02T14:50:35Z has_accepted_license: '1' keyword: - Software - Unified modeling language - Requirements engineering - Ontologies - Search problems - Natural languages language: - iso: eng page: 379-385 place: Piscataway, NJ, USA project: - _id: '1' name: SFB 901 - _id: '9' name: SFB 901 - Subprojekt B1 - _id: '3' name: SFB 901 - Project Area B publication: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW) publication_identifier: eisbn: - '978-1-5386-3488-2 ' isbn: - 978-1-5386-3489-9 publication_status: published publisher: IEEE quality_controlled: '1' status: public title: 'From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process' type: conference user_id: '57458' year: '2017' ... --- _id: '33312' abstract: - lang: eng text: "Mechatronic systems are used more than ever in human life. They can be found in a very wide range of domain contexts, from household appliances, and cars, to medical equipment. Mechatronic systems, as a kind of embedded systems, are the tight integration of mechanical and electrical engineering, which embed software systems. Information security of mechatronic systems has not received much attention yet. However, wherever data exists, cyber attacks threaten mechatronic systems.\r\n\r\nThe thesis focuses on the early design stages of the development of mechatronic systems. Model sequence diagrams (MSDs) are used to model requirements with real-time and safety properties. In this thesis, MSDs are extended such that security properties for example authenticity and privacy can be modeled and analyzed automatically." author: - first_name: Bahar full_name: Schwichtenberg, Bahar id: '36399' last_name: Schwichtenberg citation: ama: Schwichtenberg B. Early Prediction of Security Properties for Mechatronic Systems.; 2015. apa: Schwichtenberg, B. (2015). Early Prediction of Security Properties for Mechatronic Systems. bibtex: '@book{Schwichtenberg_2015, title={Early Prediction of Security Properties for Mechatronic Systems}, author={Schwichtenberg, Bahar}, year={2015} }' chicago: Schwichtenberg, Bahar. Early Prediction of Security Properties for Mechatronic Systems, 2015. ieee: B. Schwichtenberg, Early Prediction of Security Properties for Mechatronic Systems. 2015. mla: Schwichtenberg, Bahar. Early Prediction of Security Properties for Mechatronic Systems. 2015. short: B. Schwichtenberg, Early Prediction of Security Properties for Mechatronic Systems, 2015. date_created: 2022-09-09T11:42:25Z date_updated: 2022-12-30T22:12:13Z ddc: - '000' extern: '1' file: - access_level: closed content_type: application/pdf creator: bahareh date_created: 2022-12-30T22:10:51Z date_updated: 2022-12-30T22:10:51Z file_id: '35068' file_name: Bahar_Jazayeri_Masterarbeit.pdf file_size: 11423528 relation: main_file success: 1 file_date_updated: 2022-12-30T22:10:51Z has_accepted_license: '1' keyword: - Software Architecture - Requirements Engineering - Embedded Systems language: - iso: eng publication_status: published status: public title: Early Prediction of Security Properties for Mechatronic Systems type: mastersthesis user_id: '36399' year: '2015' ...