Semi-supervised log pattern detection and exploration using event concurrence and contextual information

X. Lu, D. Fahland, R. Andrews, S. Suriadi, M.T. Wynn, A.H.M. ter Hofstede, W.M.P. van der Aalst

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)

Abstract

Process mining offers a variety of techniques for analyzing process execution event logs. Although process discovery algorithms construct end-to-end process models, they often have difficulties dealing with the complexity of real-life event logs. Discovered models may contain either complex or over-generalized fragments, the interpretation of which is difficult, and can result in misleading insights. Detecting and visualizing behavioral patterns instead of creating model structures can reduce complexity and give more accurate insights into recorded behaviors. Unsupervised detection techniques, based on statistical properties of the log only, generate a multitude of patterns and lack domain context. Supervised pattern detection requires a domain expert to specify patterns manually and lacks the event log context. In this paper, we reconcile supervised and unsupervised pattern detection. We visualize the log and help users extract patterns of interest from the log or obtain patterns through unsupervised learning automatically. Pattern matches are visualized in the context of the event log (also showing concurrency and additional contextual information). Earlier patterns can be extended or modified based on the insights. This enables an interactive and iterative approach to identify complex and concrete behavioral patterns in event logs. We implemented our approach in the ProM framework and evaluated the tool using both the BPI Challenge 2012 log of a loan application process and an insurance claims log from a major Australian insurance company.
Original languageEnglish
Title of host publicationOn the Move to Meaningful Internet Systems. OTM 2017 Conferences
Subtitle of host publicationConfederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part I
EditorsH. Panetto, C. Debruyne, W. Gaaloul, M. Papazoglou, A. Paschke, C.A. Ardagna, R. Meersman
Place of PublicationDordrecht
PublisherSpringer
Pages154-174
Number of pages21
ISBN (Electronic)978-3-319-69462-7
ISBN (Print)978-3-319-69461-0
DOIs
Publication statusPublished - 20 Oct 2017
EventConfederated International Conference On the Move to Meaningful Internet Systems, OTM 2017 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2017 - Rhodes, Greece
Duration: 23 Sep 201727 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10573 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceConfederated International Conference On the Move to Meaningful Internet Systems, OTM 2017 held in conjunction with Conferences on CoopIS, CandTC and ODBASE 2017
CountryGreece
CityRhodes
Period23/09/1727/09/17

Keywords

  • Log pattern
  • Pattern detection
  • Semi-supervised learning

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    Lu, X., Fahland, D., Andrews, R., Suriadi, S., Wynn, M. T., ter Hofstede, A. H. M., & van der Aalst, W. M. P. (2017). Semi-supervised log pattern detection and exploration using event concurrence and contextual information. In H. Panetto, C. Debruyne, W. Gaaloul, M. Papazoglou, A. Paschke, C. A. Ardagna, & R. Meersman (Eds.), On the Move to Meaningful Internet Systems. OTM 2017 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part I (pp. 154-174). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10573 LNCS). Springer. https://doi.org/10.1007/978-3-319-69462-7_11