{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:45:31Z","timestamp":1767339931058,"version":"3.40.5"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030943424"},{"type":"electronic","value":"9783030943431"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-94343-1_4","type":"book-chapter","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T19:02:31Z","timestamp":1642964551000},"page":"43-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["ProGAN: Toward a\u00a0Framework for\u00a0Process Monitoring and\u00a0Flexibility by\u00a0Change via\u00a0Generative Adversarial Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7932-5822","authenticated-orcid":false,"given":"Maximilian","family":"Hoffmann","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6866-0799","authenticated-orcid":false,"given":"Lukas","family":"Malburg","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5515-7158","authenticated-orcid":false,"given":"Ralph","family":"Bergmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,23]]},"reference":[{"key":"4_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45759-3","volume-title":"Experience Management: Foundations, Development Methodology, and Internet-Based Applications","year":"2002","unstructured":"Bergmann, R. (ed.): Experience Management: Foundations, Development Methodology, and Internet-Based Applications. LNCS, vol. 2432. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45759-3"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.is.2012.07.005","volume":"40","author":"R Bergmann","year":"2014","unstructured":"Bergmann, R., Gil, Y.: Similarity assessment and efficient retrieval of semantic workflows. Inf. Syst. 40, 115\u2013127 (2014)","journal-title":"Inf. Syst."},{"key":"4_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-56509-4","volume-title":"Fundamentals of Business Process Management","author":"M Dumas","year":"2018","unstructured":"Dumas, M., et al.: Fundamentals of Business Process Management. Springer, Heidelberg (2018). https:\/\/doi.org\/10.1007\/978-3-662-56509-4"},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.dss.2017.04.003","volume":"100","author":"J Evermann","year":"2017","unstructured":"Evermann, J., Rehse, J., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129\u2013140 (2017)","journal-title":"Decis. Support Syst."},{"key":"4_CR5","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2672\u20132680 (2014)"},{"key":"4_CR6","unstructured":"IBM: An architectural blueprint for autonomic computing: Autonomic Computing White Paper (2006)"},{"issue":"4","key":"4_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MSMC.2020.3003135","volume":"6","author":"C Janiesch","year":"2020","unstructured":"Janiesch, C., et al.: The Internet of Things meets business process management: a manifesto. IEEE Syst. Man Cybern. Mag. 6(4), 34\u201344 (2020)","journal-title":"IEEE Syst. Man Cybern. Mag."},{"key":"4_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-030-29249-2_11","volume-title":"Case-Based Reasoning Research and Development","author":"MT Keane","year":"2019","unstructured":"Keane, M.T., Kenny, E.M.: How case-based reasoning explains neural networks: a theoretical analysis of XAI using Post-Hoc explanation-by-example from a survey of ANN-CBR twin-systems. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 155\u2013171. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29249-2_11"},{"key":"4_CR9","unstructured":"Klein, P., Malburg, L., Bergmann, R.: FTOnto: a domain ontology for a Fischertechnik simulation production factory by reusing existing ontologies. In: Proceedings of the Conference on LWDA, vol. 2454, pp. 253\u2013264. CEUR-WS.org (2019)"},{"issue":"3","key":"4_CR10","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s12599-020-00645-0","volume":"63","author":"W Kratsch","year":"2020","unstructured":"Kratsch, W., Manderscheid, J., R\u00f6glinger, M., Seyfried, J.: Machine learning in business process monitoring: a comparison of deep learning and classical approaches used for outcome prediction. Bus. Inf. Syst. Eng. 63(3), 261\u2013276 (2020). https:\/\/doi.org\/10.1007\/s12599-020-00645-0","journal-title":"Bus. Inf. Syst. Eng."},{"key":"4_CR11","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-3-030-66498-5_8","volume-title":"Business Process Management Workshops","author":"L Malburg","year":"2020","unstructured":"Malburg, L., Seiger, R., Bergmann, R., Weber, B.: Using physical factory simulation models for business process management research. In: Del R\u00edo Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 95\u2013107. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66498-5_8"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1016\/j.procs.2021.04.009","volume":"184","author":"L Malburg","year":"2021","unstructured":"Malburg, L., et al.: Object detection for smart factory processes by machine learning. Procedia Comput. Sci. 184, 581\u2013588 (2021)","journal-title":"Procedia Comput. Sci."},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Malburg, L., Klein, P., Bergmann, R.: Semantic web services for AI-research with physical factory simulation models in Industry 4.0. In: Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL), pp. 32\u201343. SCITEPRESS (2020)","DOI":"10.5220\/0010135900320043"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Marrella, A., Mecella, M., Sardi\u00f1a, S.: Intelligent process adaptation in the SmartPM system. ACM Trans. Intell. Syst. Technol. 8(2), 25:1\u201325:43 (2017)","DOI":"10.1145\/2948071"},{"key":"4_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-030-58666-9_16","volume-title":"Business Process Management","author":"A Metzger","year":"2020","unstructured":"Metzger, A., Kley, T., Palm, A.: Triggering proactive business process adaptations via online reinforcement learning. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 273\u2013290. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58666-9_16"},{"key":"4_CR16","unstructured":"Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. CoRR abs\/1411.1784 (2014)"},{"key":"4_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-658-23559-8","volume-title":"Workflow Modeling Assistance by Case-Based Reasoning","author":"G M\u00fcller","year":"2018","unstructured":"M\u00fcller, G.: Workflow Modeling Assistance by Case-Based Reasoning. Springer, Heidelberg (2018). https:\/\/doi.org\/10.1007\/978-3-658-23559-8"},{"key":"4_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1007\/978-3-319-98648-7_29","volume-title":"Business Process Management","author":"R Poll","year":"2018","unstructured":"Poll, R., Polyvyanyy, A., Rosemann, M., R\u00f6glinger, M., Rupprecht, L.: Process forecasting: towards proactive business process management. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 496\u2013512. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98648-7_29"},{"key":"4_CR19","unstructured":"Rama-Maneiro, E., Vidal, J.C., Lama, M.: Deep Learning for Predictive Business Process Monitoring: Review and Benchmark. CoRR abs\/2009.13251 (2020)"},{"issue":"2","key":"4_CR20","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s13218-019-00586-1","volume":"33","author":"J-R Rehse","year":"2019","unstructured":"Rehse, J.-R., Mehdiyev, N., Fettke, P.: Towards explainable process predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory. KI - K\u00fcnstliche Intelligenz 33(2), 181\u2013187 (2019). https:\/\/doi.org\/10.1007\/s13218-019-00586-1","journal-title":"KI - K\u00fcnstliche Intelligenz"},{"issue":"6","key":"4_CR21","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1007\/s10270-020-00785-7","volume":"19","author":"S Sch\u00f6nig","year":"2020","unstructured":"Sch\u00f6nig, S., Ackermann, L., Jablonski, S., Ermer, A.: IoT meets BPM: a bidirectional communication architecture for IoT-aware process execution. Softw. Syst. Model. 19(6), 1443\u20131459 (2020). https:\/\/doi.org\/10.1007\/s10270-020-00785-7","journal-title":"Softw. Syst. Model."},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Seiger, R., A\u00dfmann, U.: Consistency and synchronization for workflows in cyber-physical systems. In: Proceedings of the 10th ACM\/IEEE International Conference on Cyber-Physical Systems, pp. 312\u2013313. ACM (2019)","DOI":"10.1145\/3302509.3313317"},{"issue":"2","key":"4_CR23","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s10270-017-0639-0","volume":"18","author":"R Seiger","year":"2017","unstructured":"Seiger, R., Huber, S., Heisig, P., A\u00dfmann, U.: Toward a framework for self-adaptive workflows in cyber-physical systems. Softw. Syst. Model. 18(2), 1117\u20131134 (2017). https:\/\/doi.org\/10.1007\/s10270-017-0639-0","journal-title":"Softw. Syst. Model."},{"key":"4_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/978-3-319-59536-8_30","volume-title":"Advanced Information Systems Engineering","author":"N Tax","year":"2017","unstructured":"Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477\u2013492. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59536-8_30"},{"key":"4_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-030-58666-9_14","volume-title":"Business Process Management","author":"F Taymouri","year":"2020","unstructured":"Taymouri, F., Rosa, M.L., Erfani, S., Bozorgi, Z.D., Verenich, I.: Predictive business process monitoring via generative adversarial nets: the case of next event prediction. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 237\u2013256. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58666-9_14"},{"issue":"1","key":"4_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/507984","volume":"2013","author":"WMP van der Aalst","year":"2013","unstructured":"van der Aalst, W.M.P.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013(1), 1\u201337 (2013)","journal-title":"ISRN Softw. Eng."},{"key":"4_CR27","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-030-58638-6_12","volume-title":"Business Process Management Forum","author":"S Weinzierl","year":"2020","unstructured":"Weinzierl, S., Dunzer, S., Zilker, S., Matzner, M.: Prescriptive business process monitoring for recommending next best actions. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 193\u2013209. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58638-6_12"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Wieland, M., et al.: Towards situation-aware adaptive workflows: SitOPT - a general purpose situation-aware workflow management system. In: International Conference on Pervasive Computing and Communication Workshops, pp. 32\u201337. IEEE (2015)","DOI":"10.1109\/PERCOMW.2015.7133989"}],"container-title":["Lecture Notes in Business Information Processing","Business Process Management Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-94343-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:16:37Z","timestamp":1651796197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-94343-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030943424","9783030943431"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-94343-1_4","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"type":"print","value":"1865-1348"},{"type":"electronic","value":"1865-1356"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Business Process Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bpm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bpm2021.diag.uniroma1.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"92","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"For the BPM forum 16 papers were accepted; and for the BPM and RPA Forum 8 papers were acceptedfrom 14 submissions. The BPM 2021 workshops accepted 31 full papers out of 60 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}