Detecting changes in process behavior using comparative case clustering

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

doi:10.1007/978-3-319-53435-0_3

Detecting changes in process behavior using comparative case clustering

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

Process Analytics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

15 Citations (Scopus)

5 Downloads (Pure)

Abstract

Real-life business processes are complex and often exhibit 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. 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 case 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 using both synthetic and real-life event data shows our technique can provide these insights.

Original language	English
Title of host publication	Data-Driven Process Discovery and Analysis
Subtitle of host publication	5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers
Editors	P. Ceravolo, S. Rinderle-Ma
Place of Publication	Dordrecht
Publisher	Springer
Pages	54-75
Number of pages	22
ISBN (Electronic)	978-3-319-53435-0
ISBN (Print)	978-3-319-53434-3
DOIs	https://doi.org/10.1007/978-3-319-53435-0_3
Publication status	Published - 2017
Event	5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015 - Vienna, Austria Duration: 9 Dec 2015 → 11 Dec 2015 Conference number: 5

Publication series

Name	Lecture Notes in Business Information Processing
Volume	244
ISSN (Print)	18651348

Conference

Conference	5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015
Abbreviated title	SIMPDA 2015
Country/Territory	Austria
City	Vienna
Period	9/12/15 → 11/12/15

Keywords

Concept drift
Process comparison
Process mining
Trace clustering

Access to Document

10.1007/978-3-319-53435-0_3

Cite this

Hompes, B. F. A., Buijs, J. C. A. M., van der Aalst, W. M. P., Dixit, P. M., & Buurman, J. (2017). Detecting changes in process behavior using comparative case clustering. In P. Ceravolo, & S. Rinderle-Ma (Eds.), Data-Driven Process Discovery and Analysis: 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers (pp. 54-75). (Lecture Notes in Business Information Processing; Vol. 244). Springer. https://doi.org/10.1007/978-3-319-53435-0_3

Hompes, B.F.A. ; Buijs, J.C.A.M. ; van der Aalst, W.M.P. et al. / Detecting changes in process behavior using comparative case clustering. Data-Driven Process Discovery and Analysis: 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. editor / P. Ceravolo ; S. Rinderle-Ma . Dordrecht : Springer, 2017. pp. 54-75 (Lecture Notes in Business Information Processing).

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abstract = "Real-life business processes are complex and often exhibit 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. 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 case 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 using both synthetic and real-life event data shows our technique can provide these insights.",

keywords = "Concept drift, Process comparison, Process mining, Trace clustering",

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year = "2017",

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Hompes, BFA, Buijs, JCAM, van der Aalst, WMP, Dixit, PM & Buurman, J 2017, Detecting changes in process behavior using comparative case clustering. in P Ceravolo & S Rinderle-Ma (eds), Data-Driven Process Discovery and Analysis: 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 244, Springer, Dordrecht, pp. 54-75, 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015, Vienna, Austria, 9/12/15. https://doi.org/10.1007/978-3-319-53435-0_3

Detecting changes in process behavior using comparative case clustering. / Hompes, B.F.A.; Buijs, J.C.A.M.; van der Aalst, W.M.P. et al.
Data-Driven Process Discovery and Analysis: 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. ed. / P. Ceravolo; S. Rinderle-Ma . Dordrecht: Springer, 2017. p. 54-75 (Lecture Notes in Business Information Processing; Vol. 244).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Buijs, J.C.A.M.

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AU - Buurman, J.

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AB - Real-life business processes are complex and often exhibit 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. 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 case 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 using both synthetic and real-life event data shows our technique can provide these insights.

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Hompes BFA, Buijs JCAM, van der Aalst WMP, Dixit PM, Buurman J. Detecting changes in process behavior using comparative case clustering. In Ceravolo P, Rinderle-Ma S, editors, Data-Driven Process Discovery and Analysis: 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. Dordrecht: Springer. 2017. p. 54-75. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-319-53435-0_3

Detecting changes in process behavior using comparative case clustering

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