Rules on the Web. From Theory to Applications

Volume 8620 of the series Lecture Notes in Computer Science pp 281-295

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

  • Mario Luca BernardiAffiliated withUniversity of Sannio
  • , Marta CimitileAffiliated withUnitelma Sapienza University
  • , Chiara Di FrancescomarinoAffiliated withFBK-IRST
  • , Fabrizio Maria MaggiAffiliated withUniversity of Tartu

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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.


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