{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T22:04:01Z","timestamp":1766181841671,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030985806"},{"type":"electronic","value":"9783030985813"}],"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":82,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Process mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as \u201ckey items\u201d, meaning the auditors wants to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing.<\/jats:p>","DOI":"10.1007\/978-3-030-98581-3_13","type":"book-chapter","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T18:03:23Z","timestamp":1648058603000},"page":"167-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Active Anomaly Detection for\u00a0Key Item Selection in\u00a0Process Auditing"],"prefix":"10.1007","author":[{"given":"Ruben","family":"Post","sequence":"first","affiliation":[]},{"given":"Iris","family":"Beerepoot","sequence":"additional","affiliation":[]},{"given":"Xixi","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Stijn","family":"Kas","sequence":"additional","affiliation":[]},{"given":"Sebastiaan","family":"Wiewel","sequence":"additional","affiliation":[]},{"given":"Angelique","family":"Koopman","sequence":"additional","affiliation":[]},{"given":"Hajo","family":"Reijers","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining: Data Science in Action","author":"WMP Van Der Aalst","year":"2016","unstructured":"Van Der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49851-4","edition":"2"},{"issue":"1","key":"13_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.accinf.2012.06.015","volume":"14","author":"M Jans","year":"2013","unstructured":"Jans, M., Alles, M., Vasarhelyi, M.: The case for process mining in auditing: Sources of value added and areas of application. Int. J. Account. Inf. Syst. 14(1), 1\u201320 (2013)","journal-title":"Int. J. Account. Inf. Syst."},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Jans, M., Alles, M., Vasarhelyi, M.: Process mining of event logs in auditing: Opportunities and challenges. SSRN Electron. J. 1\u201332 (2010). https:\/\/ssrn.com\/abstract=1578912","DOI":"10.2139\/ssrn.2488737"},{"key":"13_CR4","unstructured":"International Standard on Auditing 450. Evaluation of misstatement identified during the audit (2009). https:\/\/www.ifac.org\/system\/files\/downloads\/a021-2010-iaasb-handbook-isa-450.pdf"},{"key":"13_CR5","volume-title":"Modern Auditing","author":"WC Boynton","year":"2001","unstructured":"Boynton, W.C., Kell, W.G., Johnson, R.N., Wheeler, S.W.: Modern Auditing, 8th edn. J. Wiley & Sons, Hoboken (2001)","edition":"8"},{"key":"13_CR6","unstructured":"Sureka, A.: Kernel based sequential data anomaly detection in business process event logs. arXiv preprint arXiv:1507.01168 (2015)"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"B\u00f6hmer, K., Rinderle-Ma, S.: Multi-perspective anomaly detection in\u00a0business process execution events. In: Debruyne, C., et al. (eds.) OTM 2016. LNCS, vol. 10033, pp. 80\u201398. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48472-3_5","DOI":"10.1007\/978-3-319-48472-3_5"},{"issue":"11","key":"13_CR8","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1007\/s10994-018-5702-8","volume":"107","author":"T Nolle","year":"2018","unstructured":"Nolle, T., Luettgen, S., Seeliger, A., M\u00fchlh\u00e4user, M.: Analyzing business process anomalies using autoencoders. Mach. Learn. 107(11), 1875\u20131893 (2018). https:\/\/doi.org\/10.1007\/s10994-018-5702-8","journal-title":"Mach. Learn."},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Pauwels, S., Calders, T.: An anomaly detection technique for business processes based on extended dynamic bayesian networks. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, pp. 494\u2013501 (2019)","DOI":"10.1145\/3297280.3297326"},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.ins.2020.11.017","volume":"549","author":"J Ko","year":"2021","unstructured":"Ko, J., Comuzzi, M.: Detecting anomalies in business process event logs using statistical leverage. Inf. Sci. 549, 53\u201367 (2021)","journal-title":"Inf. Sci."},{"key":"13_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/978-3-030-65310-1_39","volume-title":"Service-Oriented Computing","author":"G Schumann","year":"2020","unstructured":"Schumann, G., Kruse, F., Nonnenmacher, J.: A practice-oriented, control-flow-based anomaly detection approach for internal process audits. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds.) ICSOC 2020. LNCS, vol. 12571, pp. 533\u2013543. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65310-1_39"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Das, S., Wong, W.K., Dietterich, T., Fern, A., Emmott, A.: Incorporating expert feedback into active anomaly discovery. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE, pp. 853\u2013858 (2016)","DOI":"10.1109\/ICDM.2016.0102"},{"key":"13_CR13","unstructured":"van der Aalst, W.M.P., de Leoni, M., ter Hofstede, A.H.: Process mining and visual analytics: Breathing life into business process models. BPM Center Report BPM-11-15, BPMcenter. org 17, 699\u2013730 (2011)"},{"key":"13_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-3-030-26619-6_21","volume-title":"Business Process Management","author":"C Klinkm\u00fcller","year":"2019","unstructured":"Klinkm\u00fcller, C., M\u00fcller, R., Weber, I.: Mining process mining practices: an exploratory characterization of information needs in process analytics. In: Hildebrandt, T., van Dongen, B.F., R\u00f6glinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 322\u2013337. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26619-6_21"},{"issue":"2","key":"13_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301300","volume":"13","author":"I Teinemaa","year":"2019","unstructured":"Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discovery Data (TKDD) 13(2), 1\u201357 (2019)","journal-title":"ACM Trans. Knowl. Discovery Data (TKDD)"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.is.2015.07.003","volume":"56","author":"M De Leoni","year":"2016","unstructured":"De Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235\u2013257 (2016)","journal-title":"Inf. Syst."},{"key":"13_CR17","unstructured":"van Dongen, B.F.: \u201cBPI challenge 2012\u201d (2012). https:\/\/data.4tu.nl\/articles\/dataset\/BPI_Challenge_2012\/12689204\/1"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 eighth IEEE international conference on data mining. IEEE, pp. 413\u2013422 (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"13_CR19","unstructured":"Berti, A., Van Zelst, S.J. and van der Aalst, W.: Process mining for python (pm4py): bridging the gap between process-and data science. International Conference on Process Mining (2019)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Augusto, A., Conforti, R., Dumas, M., La Rosa, M.: Split miner: discovering accurate and simple business process models from event logs. In: 2017 IEEE International Conference on Data Mining (ICDM), IEEE, pp. 1\u201310 (2017)","DOI":"10.1109\/ICDM.2017.9"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"van der Aalst, W.M.P.: A practitioner\u2019s guide to process mining: limitations of the directly-follows graph (2019)","DOI":"10.1016\/j.procs.2019.12.189"}],"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\/978-3-030-98581-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T18:05:40Z","timestamp":1648058740000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98581-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030985806","9783030985813"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98581-3_13","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":"24 March 2022","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":"Eindhoven","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","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":"31 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpm2021a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpmconference.org\/2021\/category\/calls\/workshops\/","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":"65","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":"28","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":"43% - 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":"2","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":"The proceedings also include one survey paper on the results of the XES 2.9 Workshop.","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)"}}]}}