A Probabilistic Unified Framework for Event Abstraction and Process Detection from Log Data
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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.
KeywordsBusiness Process Process Instance Composition Rule Trace Length Conditioned Probability
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