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

Abstract

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.