Discovering Target-Branched Declare Constraints

  • Claudio Di Ciccio
  • Fabrizio Maria Maggi
  • Jan Mendling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8659)


Process discovery is the task of generating models from event logs. Mining processes that operate in an environment of high variability is an ongoing research challenge because various algorithms tend to produce spaghetti-like models. This is particularly the case when procedural models are generated. A promising direction to tackle this challenge is the usage of declarative process modelling languages like Declare, which summarise complex behaviour in a compact set of behavioural constraints. However, Declare constraints with branching are expensive to be calculated.In addition, it is often the case that hundreds of branching Declare constraints are valid for the same log, thus making, again, the discovery results unreadable. In this paper, we address these problems from a theoretical angle. More specifically, we define the class of Target-Branched Declare constraints and investigate the formal properties it exhibits. Furthermore, we present a technique for the efficient discovery of compact Target-Branched Declare models. We discuss the merits of our work through an evaluation based on a prototypical implementation using both artificial and real-world event logs.


Process Mining Discovery Declarative Processes 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Claudio Di Ciccio
    • 1
  • Fabrizio Maria Maggi
    • 2
  • Jan Mendling
    • 1
  1. 1.Vienna University of Business and EconomicsAustria
  2. 2.University of TartuEstonia

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