Article

Enhancing declare maps based on event correlations

Authors:

R. P. Jagadeesh Chandra Bose,

Fabrizio Maria Maggi,

Wil M. P. van der AalstAuthors Info & Claims

BPM'13: Proceedings of the 11th international conference on Business Process Management

Pages 97 - 112

https://doi.org/10.1007/978-3-642-40176-3_9

Published: 26 August 2013 Publication History

Abstract

Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.

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Cited By

Maggi FMarrella APatrizi FSkydanienko V(2023)Data-Aware Declarative Process Mining with SATACM Transactions on Intelligent Systems and Technology10.1145/360010614:4(1-26)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3600106
Bayomie DRevoredo KMendling J(2022)Multi-perspective Process Analysis: Mining the Association Between Control Flow and Data ObjectsAdvanced Information Systems Engineering10.1007/978-3-031-07472-1_5(72-89)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-07472-1_5
Figl KDi Ciccio CReijers H(2020)Do Declarative Process Models Help to Reduce Cognitive Biases Related to Business Rules?Conceptual Modeling10.1007/978-3-030-62522-1_9(119-133)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-62522-1_9
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Index Terms

Enhancing declare maps based on event correlations
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

cover image Guide Proceedings

BPM'13: Proceedings of the 11th international conference on Business Process Management

August 2013

354 pages

ISBN:9783642401756

Editors:
Florian Daniel
Department of Information Engineering and Computer Science, University of Trento, Povo, TN, Italy
,
Jianmin Wang
Institute of Information System and Engineering of School Software, Tsinghua University, Beijing, TN, China
,
Barbara Weber
Business Process Management Research Cluster, University of Innsbruck, Innsbruck, TN, Austria

Sponsors

IBMR: IBM Research
BIZAGI: Bizagi

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 August 2013

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Cited By

Maggi FMarrella APatrizi FSkydanienko V(2023)Data-Aware Declarative Process Mining with SATACM Transactions on Intelligent Systems and Technology10.1145/360010614:4(1-26)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3600106
Bayomie DRevoredo KMendling J(2022)Multi-perspective Process Analysis: Mining the Association Between Control Flow and Data ObjectsAdvanced Information Systems Engineering10.1007/978-3-031-07472-1_5(72-89)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-07472-1_5
Figl KDi Ciccio CReijers H(2020)Do Declarative Process Models Help to Reduce Cognitive Biases Related to Business Rules?Conceptual Modeling10.1007/978-3-030-62522-1_9(119-133)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-62522-1_9
Bernardi MCimitile MDistante DMartinelli FMercaldo F(2019)Dynamic malware detection and phylogeny analysis using process miningInternational Journal of Information Security10.1007/s10207-018-0415-318:3(257-284)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10207-018-0415-3
van der Aalst W(2019)Object-Centric Process Mining: Dealing with Divergence and Convergence in Event DataSoftware Engineering and Formal Methods10.1007/978-3-030-30446-1_1(3-25)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30446-1_1
Schönig SCabanillas CDi Ciccio CJablonski SMendling J(2018)Mining team compositions for collaborative work in business processesSoftware and Systems Modeling (SoSyM)10.1007/s10270-016-0567-417:2(675-693)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10270-016-0567-4
Bernardi MCimitile MDi Francescomarino CMaggi F(2016)Do activity lifecycles affect the validity of a business rule in a business process?Information Systems10.1016/j.is.2016.06.00262:C(42-59)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.is.2016.06.002
Schönig SCabanillas CJablonski SMendling J(2016)A framework for efficiently mining the organisational perspective of business processesDecision Support Systems10.1016/j.dss.2016.06.01289:C(87-97)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.dss.2016.06.012
Schönig SDi Ciccio CMaggi FMendling J(2016)Discovery of Multi-perspective Declarative Process ModelsService-Oriented Computing10.1007/978-3-319-46295-0_6(87-103)Online publication date: 10-Oct-2016
https://dl.acm.org/doi/10.1007/978-3-319-46295-0_6
Weber IFarshchi MMendling JSchneider JWainwright RCorchado JBechini AHong J(2015)Mining processes with multi-instantiationProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2699493(1231-1237)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2699493

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