A probabilistic unified framework for event abstraction and process detection from log data

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 Φ. Specifically, an interpretation is a pair (σ,W) that provides a two-level “explanation” for Φ: σ is a sequence of activities that may have triggered the events in Φ, and W is a process whose model admits σ. To solve this problem, we propose a probabilistic framework representing “consistent” Φ’s interpretations, where each interpretation is associated with a probability score.