International Workshop on Rules and Rule Markup Languages for the Semantic Web

RuleML 2014: Rules on the Web. From Theory to Applications pp 281-295

Using Discriminative Rule Mining to Discover Declarative Process Models with Non-atomic Activities

  • Mario Luca Bernardi
  • Marta Cimitile
  • Chiara Di Francescomarino
  • Fabrizio Maria Maggi
Conference paper

DOI: 10.1007/978-3-319-09870-8_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8620)
Cite this paper as:
Bernardi M.L., Cimitile M., Di Francescomarino C., Maggi F.M. (2014) Using Discriminative Rule Mining to Discover Declarative Process Models with Non-atomic Activities. In: Bikakis A., Fodor P., Roman D. (eds) Rules on the Web. From Theory to Applications. RuleML 2014. Lecture Notes in Computer Science, vol 8620. Springer, Cham

Abstract

Process discovery techniques try to generate process models from execution logs. Declarative process modeling languages are more suitable than procedural notations for representing the discovery results deriving from logs of processes working in dynamic and low-predictable environments. However, existing declarative discovery approaches aim at mining declarative specifications considering each activity in a business process as an atomic/instantaneous event. In spite of this, often, in realistic environments, process activities are not instantaneous; rather, their execution spans across a time interval and is characterized by a sequence of states of a transactional lifecycle. In this paper, we investigate how to use discriminative rule mining in the discovery task, to characterize lifecycles that determine constraint violations and lifecycles that ensure constraint fulfillments. The approach has been implemented as a plug-in of the process mining tool ProM and validated on synthetic logs and on a real-life log recorded by an incident and problem management system called VINST in use at Volvo IT Belgium.

Keywords

Process Discovery Rule Mining Discriminative Mining Non-Atomic Activities Activity lifecycle Linear Temporal Logic 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mario Luca Bernardi
    • 1
  • Marta Cimitile
    • 2
  • Chiara Di Francescomarino
    • 3
  • Fabrizio Maria Maggi
    • 4
  1. 1.University of SannioBeneventoItaly
  2. 2.Unitelma Sapienza UniversityRomeItaly
  3. 3.FBK-IRSTTrentoItaly
  4. 4.University of TartuTartuEstonia

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