Towards a holistic discovery of decisions in process-aware information systems

Johannes De Smedt, Faruk Hasic, Seppe vanden Broucke, Jan Vanthienen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The interest of integrating decision analysis approaches with the automated discovery of processes from data has seen a vast surge over the past few years. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group has provided a suitable solution for filling the void of decision representation in business process modeling languages. Process discovery has already embraced DMN for so-called decision mining, however, the efforts are still limited to a control flow point of view, i.e., explaining routing (constructs) or decision points. This work, however, introduces an integrated way of capturing the decisions that are embedded in the process, which is not limited to local characteristics, but provides a decision model in the form of a decision diagram which encompasses the full process execution span. Therefore, a typology is proposed for classifying different activities that contribute to the decision dimension of the process. This enables the possibility for an in-depth analysis of every activity, deciding whether it entails a decision, and what its relation is to other activities. The findings are implemented and illustrated on the 2013 BPI Challenge log, an exemplary dataset originating from a decision-driven process.
Original languageEnglish
Title of host publicationBusiness Process Management
Subtitle of host publication15th International Conference, BPM 2017 Barcelona, Spain, September 10–15, 2017 Proceedings
EditorsJosep Carmona, Gregor Engels, Akhil Kumar
Place of PublicationCham
PublisherSpringer
Pages183-199
Number of pages17
Edition1
ISBN (Electronic)9783319650005
ISBN (Print)9783319649993
DOIs
Publication statusPublished - 31 Dec 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords / Materials (for Non-textual outputs)

  • decision mining
  • decision model and notation
  • process mining

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