Discovering causal factors explaining business process performance variation

B.F.A. Hompes; Abderrahmane Maaradji; M. La Rosa; M. Dumas; J.C.A.M. Buijs; W.M.P. van der Aalst

doi:10.1007/978-3-319-59536-8_12

Discovering causal factors explaining business process performance variation

B.F.A. Hompes
, Abderrahmane Maaradji
, M. La Rosa
, M. Dumas
, J.C.A.M. Buijs
, W.M.P. van der Aalst

Process Analytics

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

41 Citations (Scopus)

501 Downloads (Pure)

Abstract

Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

Original language	English
Title of host publication	Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017
Editors	Eric Dubois, Klaus Pohl
Publisher	Springer
Pages	177-191
Number of pages	15
ISBN (Print)	9783319595351
DOIs	https://doi.org/10.1007/978-3-319-59536-8_12
Publication status	Published - 12 Jun 2017
Event	29th International Conference on Advanced Information Systems Engineering, CAiSE 2017 - Essen, Essen, Germany Duration: 12 Jun 2017 → 16 Jun 2017 Conference number: 29 http://caise2017.paluno.de/welcome/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10253 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th International Conference on Advanced Information Systems Engineering, CAiSE 2017
Abbreviated title	CAiSE 2017
Country/Territory	Germany
City	Essen
Period	12/06/17 → 16/06/17
Internet address	http://caise2017.paluno.de/welcome/

Keywords

process mining
causal factor
time series
business process

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10.1007/978-3-319-59536-8_12

Publishers versionFinal published version, 320 KB

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Hompes, B. F. A., Maaradji, A., La Rosa, M., Dumas, M., Buijs, J. C. A. M., & van der Aalst, W. M. P. (2017). Discovering causal factors explaining business process performance variation. In E. Dubois, & K. Pohl (Eds.), Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017 (pp. 177-191). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10253 LNCS). Springer. https://doi.org/10.1007/978-3-319-59536-8_12

Hompes, B.F.A. ; Maaradji, Abderrahmane ; La Rosa, M. et al. / Discovering causal factors explaining business process performance variation. Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. editor / Eric Dubois ; Klaus Pohl. Springer, 2017. pp. 177-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.",

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Hompes, BFA, Maaradji, A, La Rosa, M, Dumas, M, Buijs, JCAM & van der Aalst, WMP 2017, Discovering causal factors explaining business process performance variation. in E Dubois & K Pohl (eds), Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10253 LNCS, Springer, pp. 177-191, 29th International Conference on Advanced Information Systems Engineering, CAiSE 2017, Essen, Germany, 12/06/17. https://doi.org/10.1007/978-3-319-59536-8_12

Discovering causal factors explaining business process performance variation. / Hompes, B.F.A.; Maaradji, Abderrahmane; La Rosa, M. et al.
Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. ed. / Eric Dubois; Klaus Pohl. Springer, 2017. p. 177-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10253 LNCS).

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

TY - GEN

T1 - Discovering causal factors explaining business process performance variation

AU - Hompes, B.F.A.

AU - Maaradji, Abderrahmane

AU - La Rosa, M.

AU - Dumas, M.

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

AU - van der Aalst, W.M.P.

N1 - Conference code: 29

PY - 2017/6/12

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N2 - Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

AB - Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

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KW - causal factor

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SN - 9783319595351

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017

A2 - Dubois, Eric

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ER -

Hompes BFA, Maaradji A, La Rosa M, Dumas M, Buijs JCAM, van der Aalst WMP. Discovering causal factors explaining business process performance variation. In Dubois E, Pohl K, editors, Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. Springer. 2017. p. 177-191. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59536-8_12

Discovering causal factors explaining business process performance variation

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Keywords

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