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Process mining is a discipline which concerns the analysis of execution dataof operational processes, the extraction of models from event data, themeasurement of the conformance between event data and normative models, and theenhancement of all aspects of processes. Most approaches assume that event datais accurately capture behavior. However, this is not realistic in manyapplications: data can contain uncertainty, generated from errors in recording,imprecise measurements, and other factors. Recently, new methods have beendeveloped to analyze event data containing uncertainty; these techniquesprominently rely on representing uncertain event data by means of graph-basedmodels explicitly capturing uncertainty. In this paper, we introduce a newapproach to efficiently calculate a graph representation of the behaviorcontained in an uncertain process trace. We present our novel algorithm, proveits asymptotic time complexity, and show experimental results that highlightorder-of-magnitude performance improvements for the behavior graphconstruction.

Efficient Time and Space Representation of Uncertain Event Data