Instance-based process matching using event-log information

Han Van Der Aa; Avigdor Gal; Henrik Leopold; Hajo A. Reijers; Tomer Sagi; Roee Shraga

doi:10.1007/978-3-319-59536-8_18

Instance-based process matching using event-log information

Han Van Der Aa
, Avigdor Gal
, Henrik Leopold
, Hajo A. Reijers
, Tomer Sagi
, Roee Shraga

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

Abstract

Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.

Original language	English
Title of host publication	Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017, Proceedings
Editors	Eric Dubois, Klaus Pohl
Publisher	Springer Verlag
Pages	283-297
Number of pages	15
ISBN (Print)	9783319595351
DOIs	https://doi.org/10.1007/978-3-319-59536-8_18
State	Published - 2017
Externally published	Yes
Event	29th International Conference on Advanced Information Systems Engineering, CAiSE 2017 - Essen, Germany Duration: 12 Jun 2017 → 16 Jun 2017

Publication series

Name	Lecture Notes in Computer Science
Volume	10253 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th International Conference on Advanced Information Systems Engineering, CAiSE 2017
Country/Territory	Germany
City	Essen
Period	12/06/17 → 16/06/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Event logs
Process model matching
Process similarity

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-59536-8_18

Cite this

Van Der Aa, H., Gal, A., Leopold, H., Reijers, H. A., Sagi, T., & Shraga, R. (2017). Instance-based process matching using event-log information. In E. Dubois, & K. Pohl (Eds.), Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017, Proceedings (pp. 283-297). (Lecture Notes in Computer Science; Vol. 10253 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59536-8_18

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title = "Instance-based process matching using event-log information",

abstract = "Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.",

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note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 29th International Conference on Advanced Information Systems Engineering, CAiSE 2017 ; Conference date: 12-06-2017 Through 16-06-2017",

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doi = "10.1007/978-3-319-59536-8\_18",

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Van Der Aa, H, Gal, A, Leopold, H, Reijers, HA, Sagi, T & Shraga, R 2017, Instance-based process matching using event-log information. in E Dubois & K Pohl (eds), Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017, Proceedings. Lecture Notes in Computer Science, vol. 10253 LNCS, Springer Verlag, pp. 283-297, 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_18

Instance-based process matching using event-log information. / Van Der Aa, Han; Gal, Avigdor; Leopold, Henrik et al.
Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017, Proceedings. ed. / Eric Dubois; Klaus Pohl. Springer Verlag, 2017. p. 283-297 (Lecture Notes in Computer Science; Vol. 10253 LNCS).

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

TY - GEN

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AB - Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.

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ER -

Instance-based process matching using event-log information

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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