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Efficient Construction of Behavior Graphs for Uncertain Event Data

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 389)

Abstract

The discipline of process mining deals with analyzing execution data of operational processes, extracting models from event data, checking the conformance between event data and normative models, and enhancing all aspects of processes. Recently, new techniques have been developed to analyze event data containing uncertainty; these techniques strongly rely on representing uncertain event data through graph-based models capturing uncertainty. In this paper we present a novel approach to efficiently compute a graph representation of the behavior contained in an uncertain process trace. We present our new algorithm, analyze its time complexity, and report experimental results showing order-of-magnitude performance improvements for behavior graph construction.

Keywords

Process mining Uncertain data Event data representation 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Process and Data Science Group (PADS) Department of Computer ScienceRWTH Aachen UniversityAachenGermany

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