A Probabilistic Unified Framework for Event Abstraction and Process Detection from Log Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9415)


We consider the scenario where the executions of different business processes are traced into a log, where each trace describes a process instance as a sequence of low-level events (representing basic kinds of operations). In this context, we address a novel problem: given a description of the processes’ behaviors in terms of high-level activities (instead of low-level events), and in the presence of uncertainty in the mapping between events and activities, find all the interpretations of each trace \(\Phi \). Specifically, an interpretation is a pair \(\langle \sigma , W \rangle \) that provides a two-level “explanation” for \(\Phi \): \(\sigma \) is a sequence of activities that may have triggered the events in \(\Phi \), and W is a process whose model admits \(\sigma \). To solve this problem, we propose a probabilistic framework representing “consistent” \(\Phi \)’s interpretations, where each interpretation is associated with a probability score.


Business Process Process Instance Composition Rule Trace Length Conditioned Probability 
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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.DIMESUniversity of CalabriaRendeItaly
  2. 2.ICAR-CNRRendeItaly

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