Detecting change in processes using comparative trace clustering

B.F.A. Hompes, J.C.A.M. Buijs, W.M.P. van der Aalst, P.M. Dixit, J. Buurman

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

21 Citations (Scopus)
101 Downloads (Pure)

Abstract

Real-life business processes are complex and show a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Besides changes over time, case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary flexible business processes. This paper presents a novel comparative trace clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation on real-life event data shows our technique can provide these insights.

Original languageEnglish
Title of host publicationProceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, December 9-11, 2015
EditorsP. Caravolo, S. Rinderle-Ma
PublisherCEUR-WS.org
Pages95-108
Number of pages14
Publication statusPublished - 2015
Event5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015 - Vienna, Austria
Duration: 9 Dec 201511 Dec 2015
Conference number: 5

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR
Volume1527
ISSN (Print)1613-0073

Conference

Conference5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015
Abbreviated titleSIMPDA 2015
Country/TerritoryAustria
CityVienna
Period9/12/1511/12/15

Keywords

  • Concept Drift
  • Process Comparison
  • Process mining
  • Trace Clustering

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