Is my event log complete? — A probabilistic approach to process mining

Abstract

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Statistical relational learning for workflow mining

“…Against the assumption of working with 'complete' logs, [54] extends Petri nets with a weight function that allows to compute the probability of a transition to fire given a marking, and of a sequence of transitions (process trace) to fire. A probability distribution Π over all the possible transition matrices for traces and a probabilistic lower bound for a log to be complete are defined for three subclasses of Petri nets.…”

“…The approaches [21,54,55] all deal with procedural model languages such as Petri nets, while we focus on logic-based declarative languages, and are more concerned with computing a probability associated with the log than a probability of the compliance of traces.…”

An Approach to Evaluate the Local Completeness of an Event Log

“…The definition of occurrence frequency of a DS is similar to that in [5] but presented in a direct and formal way.…”

“…Two variants of the approach proposed in [5] are referred to as KZN 1 and KZN 2 respectively. The confidence level is set to be 0.95 for estimations by these variants during experiments.…”

“…An algorithm has been proposed to judge whether an event log is locally complete or not by van Hee et al [5].…”