Efficient Time and Space Representation of Uncertain Event Data

Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that ev...

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Bibliographic Details
Published inAlgorithms Vol. 13; no. 11; p. 285
Main Authors Pegoraro, Marco, Uysal, Merih Seran, van der Aalst, Wil M. P.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2020
Subjects
Algorithms
Clinical trials
Data analysis
Graphical representations
Information systems
partial order
process mining
Software
Thrombocytopenia
uncertain data
Uncertainty
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Summary:Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately captured behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction.
ISSN:1999-4893
1999-4893
DOI:10.3390/a13110285