{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T15:26:01Z","timestamp":1726154761583},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030959463"},{"type":"electronic","value":"9783030959470"}],"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\/978-3-030-95947-0_28","type":"book-chapter","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T13:11:21Z","timestamp":1644930681000},"page":"399-413","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Counterfactual Explanations for\u00a0Predictive Business Process Monitoring"],"prefix":"10.1007","author":[{"given":"Tsung-Hao","family":"Huang","sequence":"first","affiliation":[]},{"given":"Andreas","family":"Metzger","sequence":"additional","affiliation":[]},{"given":"Klaus","family":"Pohl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"28_CR1","unstructured":"Blevi, L., Delporte, L., Robbrecht, J.: Process mining on the loan application process of a Dutch financial institute. BPI Challenge (2017)"},{"key":"28_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/978-3-030-49435-3_18","volume-title":"Advanced Information Systems Engineering","author":"K B\u00f6hmer","year":"2020","unstructured":"B\u00f6hmer, K., Rinderle-Ma, S.: LoGo: combining local and global techniques for predictive business process monitoring. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) CAiSE 2020. LNCS, vol. 12127, pp. 283\u2013298. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49435-3_18"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Breuker, D., Matzner, M., Delfmann, P., Becker, J.: Comprehensible predictive models for business processes. MIS Q. 40(4), 1009\u20131034 (2016)","DOI":"10.25300\/MISQ\/2016\/40.4.10"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Byrne, R.M.J.: Counterfactuals in explainable artificial intelligence (XAI): evidence from human reasoning. In: The Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019. ijcai.org (2019)","DOI":"10.24963\/ijcai.2019\/876"},{"key":"28_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-030-26619-6_19","volume-title":"Business Process Management","author":"M Camargo","year":"2019","unstructured":"Camargo, M., Dumas, M., Gonz\u00e1lez-Rojas, O.: Learning accurate LSTM models of business processes. In: Hildebrandt, T., van Dongen, B.F., R\u00f6glinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 286\u2013302. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26619-6_19"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Evermann, J., Rehse, J., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129\u2013140 (2017)","DOI":"10.1016\/j.dss.2017.04.003"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Francescomarino, C.D., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. IEEE Trans. Serv. Comput. 12(6), 896\u2013909 (2019)","DOI":"10.1109\/TSC.2016.2645153"},{"key":"28_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/978-3-319-98648-7_27","volume-title":"Business Process Management","author":"C Di Francescomarino","year":"2018","unstructured":"Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462\u2013479. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98648-7_27"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Galanti, R., Coma-Puig, B., de Leoni, M., Carmona, J., Navarin, N.: Explainable predictive process monitoring. In: ICPM 2020. IEEE (2020)","DOI":"10.1109\/ICPM49681.2020.00012"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., Turini, F.: Factual and counterfactual explanations for black box decision making. IEEE Intell. Syst. 34(6), 14\u201323 (2019)","DOI":"10.1109\/MIS.2019.2957223"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 93:1\u201393:42 (2019)","DOI":"10.1145\/3236009"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Harl, M., Weinzierl, S., Stierle, M., Matzner, M.: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst., 1\u201316 (2020)","DOI":"10.1080\/12460125.2020.1780780"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Jan, S.T.K., Ishakian, V., Muthusamy, V.: AI trust in business processes: the need for process-aware explanations. In: Conference on Artificial Intelligence, AAAI 2020, pp. 13403\u201313404. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i08.7056"},{"key":"28_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-319-07881-6_31","volume-title":"Advanced Information Systems Engineering","author":"FM Maggi","year":"2014","unstructured":"Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457\u2013472. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07881-6_31"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"M\u00e1rquez-Chamorro, A.E., Resinas, M., Ruiz-Cort\u00e9s, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. 11(6), 962\u2013977 (2018)","DOI":"10.1109\/TSC.2017.2772256"},{"key":"28_CR16","unstructured":"Mehdiyev, N., Fettke, P.: Prescriptive process analytics with deep learning and explainable artificial intelligence. In: 28th European Conference on Information Systems, ECIS 2020 (2020)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019)","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Molnar, C.: Interpretable Machine Learning. Online (2019)","DOI":"10.21105\/joss.00786"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Park, G., Song, M.: Predicting performances in business processes using deep neural networks. Decis. Support Syst. 129 (2020)","DOI":"10.1016\/j.dss.2019.113191"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Rehse, J., Mehdiyev, N., Fettke, P.: Towards explainable process predictions for industry 4.0 in the dfki-smart-lego-factory. K\u00fcnstliche Intell. 33(2), 181\u2013187 (2019)","DOI":"10.1007\/s13218-019-00586-1"},{"key":"28_CR21","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning. In: ICML Workshop on Human Interpretability in Machine Learning (WHI) (2016)"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: explaining the predictions of any classifier. In: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"28_CR23","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-030-58638-6_9","volume-title":"Business Process Management Forum","author":"W Rizzi","year":"2020","unstructured":"Rizzi, W., Di Francescomarino, C., Maggi, F.M.: Explainability in predictive process monitoring: when understanding helps improving. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 141\u2013158. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58638-6_9"},{"key":"28_CR24","unstructured":"Rodrigues, A., et al.: Stairway to value: mining a loan application process. BPI Challenge (2017)"},{"key":"28_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/978-3-030-58666-9_15","volume-title":"Business Process Management","author":"R Sindhgatta","year":"2020","unstructured":"Sindhgatta, R., Moreira, C., Ouyang, C., Barros, A.: Exploring interpretable predictive models for business processes. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 257\u2013272. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58666-9_15"},{"key":"28_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/978-3-030-65310-1_31","volume-title":"Service-Oriented Computing","author":"R Sindhgatta","year":"2020","unstructured":"Sindhgatta, R., Ouyang, C., Moreira, C.: Exploring interpretability for predictive process analytics. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds.) ICSOC 2020. LNCS, vol. 12571, pp. 439\u2013447. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65310-1_31"},{"issue":"6","key":"28_CR27","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1007\/s10270-020-00789-3","volume":"19","author":"N Tax","year":"2020","unstructured":"Tax, N., Teinemaa, I., van Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. Softw. Syst. Model. 19(6), 1345\u20131365 (2020). https:\/\/doi.org\/10.1007\/s10270-020-00789-3","journal-title":"Softw. Syst. Model."},{"key":"28_CR28","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":"28_CR29","doi-asserted-by":"crossref","unstructured":"Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17:1\u201317:57 (2019)","DOI":"10.1145\/3301300"},{"key":"28_CR30","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1007\/978-3-319-42887-1_18","volume-title":"Business Process Management Workshops","author":"I Verenich","year":"2016","unstructured":"Verenich, I., Dumas, M., La Rosa, M., Maggi, F.M., Di Francescomarino, C.: Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 218\u2013229. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-42887-1_18"},{"key":"28_CR31","doi-asserted-by":"crossref","unstructured":"Verenich, I., Dumas, M., La Rosa, M., Nguyen, H.: Predicting process performance: a white-box approach based on process models. J. Softw. Evol. Process 31(6) (2019)","DOI":"10.1002\/smr.2170"},{"key":"28_CR32","doi-asserted-by":"crossref","unstructured":"Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Trans. Intell. Syst. Technol. 10(4), 34:1\u201334:34 (2019)","DOI":"10.1145\/3331449"},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL & Tech. 31, 841 (2017)","DOI":"10.2139\/ssrn.3063289"},{"key":"28_CR34","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-030-66498-5_10","volume-title":"Business Process Management Workshops","author":"S Weinzierl","year":"2020","unstructured":"Weinzierl, S., Zilker, S., Brunk, J., Revoredo, K., Matzner, M., Becker, J.: XNAP: making LSTM-based next activity predictions explainable by using LRP. In: Del R\u00edo Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 129\u2013141. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66498-5_10"}],"container-title":["Lecture Notes in Business Information Processing","Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95947-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T13:32:15Z","timestamp":1644931935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95947-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030959463","9783030959470"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95947-0_28","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":"16 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EMCIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European, Mediterranean, and Middle Eastern Conference on Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"emcis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/emcis.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"155","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":"54","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":"35% - 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":"2","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":"2.5","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)"}}]}}