{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:20:26Z","timestamp":1772670026423,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031561061","type":"print"},{"value":"9783031561078","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56107-8_15","type":"book-chapter","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T12:02:06Z","timestamp":1712923326000},"page":"191-203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Uncovering the\u00a0Hidden Significance of\u00a0Activities Location in\u00a0Predictive Process Monitoring"],"prefix":"10.1007","author":[{"given":"Mozhgan","family":"Vazifehdoostirani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohsen","family":"Abbaspour Onari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isel","family":"Grau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Genga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Remco","family":"Dijkman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence: concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"15_CR3","doi-asserted-by":"publisher","unstructured":"Buliga, A., Di Francescomarino, C., Ghidini, C., Maggi, F.M.: Counterfactuals and ways to build them: evaluating approaches in predictive process monitoring. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds.) Advanced Information Systems Engineering. CAiSE 2023. LNCS, vol. 13901, pp. 558\u2013574. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-34560-9_33","DOI":"10.1007\/978-3-031-34560-9_33"},{"issue":"6","key":"15_CR4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.3390\/a15060199","volume":"15","author":"G El-khawaga","year":"2022","unstructured":"El-khawaga, G., Abu-Elkheir, M., Reichert, M.: XAI in the context of predictive process monitoring: an empirical analysis framework. Algorithms 15(6), 199 (2022)","journal-title":"Algorithms"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Elkhawaga, G., Abu-Elkheir, M., Reichert, M.: Explainability of predictive process monitoring results: can you see my data issues? Appl. Sci. 12(16) (2022)","DOI":"10.3390\/app12168192"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Fehrer, T., Fischer, D.A., Leemans, S.J., R\u00f6glinger, M., Wynn, M.T.: An assisted approach to business process redesign. Decis. Support Syst. 156 (2022)","DOI":"10.1016\/j.dss.2022.113749"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Harane, N., Rathi, S.: Comprehensive survey on deep learning approaches in predictive business process monitoring. Modern Approaches in Machine Learning and Cognitive Science, pp. 115\u2013128 (2020)","DOI":"10.1007\/978-3-030-38445-6_9"},{"key":"15_CR8","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1080\/12460125.2020.1780780","volume":"29","author":"M Harl","year":"2020","unstructured":"Harl, M., Weinzierl, S., Stierle, M., Matzner, M.: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst. 29, 312\u2013327 (2020)","journal-title":"J. Decis. Syst."},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Hsieh, C., Moreira, C., Ouyang, C.: Dice4el: interpreting process predictions using a milestone-aware counterfactual approach. In: International Conference on Process Mining, pp. 88\u201395. IEEE (2021)","DOI":"10.1109\/ICPM53251.2021.9576881"},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"Huang, T.H., Metzger, A., Pohl, K.: Counterfactual explanations for predictive business process monitoring. In: Themistocleous, M., Papadaki, M. (eds.) Information Systems. EMCIS 2021. LNBIP, vol. 437, pp. 399\u2013413. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-95947-0_28","DOI":"10.1007\/978-3-030-95947-0_28"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Hundogan, O., Lu, X., Du, Y., Reijers, H.A.: CREATED: generating viable counterfactual sequences for predictive process analytics. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds.) Advanced Information Systems Engineering. CAiSE 2023. LNCS, vol. 13901, pp. 541\u2013557. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-34560-9_32","DOI":"10.1007\/978-3-031-34560-9_32"},{"issue":"6","key":"15_CR12","doi-asserted-by":"publisher","first-page":"187","DOI":"10.3390\/a15060187","volume":"15","author":"S Lee","year":"2022","unstructured":"Lee, S., Comuzzi, M., Kwon, N.: Exploring the suitability of rule-based classification to provide interpretability in outcome-based process predictive monitoring. Algorithms 15(6), 187 (2022)","journal-title":"Algorithms"},{"key":"15_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-319-23063-4_21","volume-title":"Business Process Management","author":"A Leontjeva","year":"2015","unstructured":"Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297\u2013313. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-23063-4_21"},{"key":"15_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.103994","volume":"127","author":"J Munoz-Gama","year":"2022","unstructured":"Munoz-Gama, J., et al.: Process mining for healthcare: characteristics and challenges. J. Biomed. Inform. 127, 103994 (2022)","journal-title":"J. Biomed. Inform."},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Pasquadibisceglie, V., Castellano, G., Appice, A., Malerba, D.: Fox: a neuro-fuzzy model for process outcome prediction and explanation. In: International Conference on Process Mining, pp. 112\u2013119. IEEE (2021)","DOI":"10.1109\/ICPM53251.2021.9576678"},{"key":"15_CR16","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":"15_CR17","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":"15_CR18","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) (2019)","DOI":"10.1145\/3301300"},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":"Vazifehdoostirani, M., Genga, L., Lu, X., Verhoeven, R., van Laarhoven, H., Dijkman, R.: Interactive multi-interest process pattern discovery. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds.) Business Process Management. BPM 2023. LNCS, vol. 14159, pp. 303\u2013319. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-41620-0_18","DOI":"10.1007\/978-3-031-41620-0_18"},{"key":"15_CR20","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"},{"key":"15_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108773","volume":"248","author":"B Wickramanayake","year":"2022","unstructured":"Wickramanayake, B., He, Z., Ouyang, C., Moreira, C., Xu, Y., Sindhgatta, R.: Building interpretable models for business process prediction using shared and specialised attention mechanisms. Knowl.-Based Syst. 248, 108773 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"15_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106678","volume":"125","author":"B Wickramanayake","year":"2023","unstructured":"Wickramanayake, B., Ouyang, C., Xu, Y., Moreira, C.: Generating multi-level explanations for process outcome predictions. Eng. Appl. Artif. Intell. 125, 106678 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"13","key":"15_CR23","doi-asserted-by":"publisher","first-page":"6390","DOI":"10.3390\/app12136390","volume":"12","author":"M Yang","year":"2022","unstructured":"Yang, M., et al.: Design and implementation of an explainable bidirectional LSTM model based on transition system approach for cooperative AI-workers. Appl. Sci. 12(13), 6390 (2022)","journal-title":"Appl. Sci."},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Yuan, X.: An improved apriori algorithm for mining association rules. In: AIP Conference Proceedings, vol. 1820. AIP Publishing (2017)","DOI":"10.1063\/1.4977361"}],"container-title":["Lecture Notes in Business Information Processing","Process Mining Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56107-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T12:04:05Z","timestamp":1712923445000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56107-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031561061","9783031561078"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56107-8_15","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Process Mining","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpm2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpmconference.org\/2023\/","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":"85","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":"38","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":"1","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":"45% - 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":"3","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":"1.82","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":"15 sub-reviewers supported the PC members under their invitation","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)"}}]}}