research-article

Mining processes with multi-instantiation

Authors:

Mostafa Farshchi,

Jean-Guy SchneiderAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 1231 - 1237

https://doi.org/10.1145/2695664.2699493

Published: 13 April 2015 Publication History

Abstract

Process mining, in particular discovering process models by mining event traces, is becoming a widely adopted practice. However, when the underlying process contains subprocesses which are instantiated multiple times in parallel, classical process mining techniques that assume a flat process are not directly applicable. Their application can cause one of two problems: either the mined model is overly general, allowing arbitrary order and execution frequency of activities in the sub-process, or it lacks fitness by capturing only single instantiation of sub-processes. For conformance checking, this results in a too high rate of either false positives or false negatives, respectively. In this paper, we propose an extension to well-known process mining techniques, adding the capability of handling multi-instantiated sub-processes to discovery and conformance checking. We evaluate the approach with a real-world data set.

References

[1]

T. Baier and J. Mendling. Bridging abstraction layers in process mining by automated matching of events and activities. In BPM, pages 17--32, 2013.

Digital Library

[2]

J. Bauckmann, U. Leser, F. Naumann, and V. Tietz. Efficiently detecting inclusion dependencies. In IEEE ICDE, pages 1448--1450, 2007.

[3]

R. Bose, F. Maggi, and W. van der Aalst. Enhancing declare maps based on event correlations. In BPM, 2013.

Digital Library

[4]

R. Conforti, M. Dumas, L. García-Bañuelos, and M. L. Rosa. Beyond tasks and gateways: Discovering BPMN models with subprocesses, boundary events and multi-instance markers. In BPM, 2014.

[5]

D. Fahland, M. de Leoni, B. F. van Dongen, and W. M. P. van der Aalst. Conformance checking of interacting processes with overlapping instances. In BPM, pages 345--361, 2011.

Digital Library

[6]

D. Fahland and W. M. P. van der Aalst. Simplifying discovered process models in a controlled manner. Information Systems, 38(4):585--605, 2013.

Digital Library

[7]

K. Gerke, A. Claus, and J. Mendling. Process mining of RFID-based supply chains. In B. Hofreiter and H. Werthner, editors, IEEE Conference on Commerce and Enterprise Computing, CEC, 2009.

Digital Library

[8]

G. Greco, A. Guzzo, and L. Pontieri. Mining taxonomies of process models. Data & Knowledge Engineering, 67(1):74--102, Oct. 2008.

Digital Library

[9]

C. W. Günther, A. Rozinat, and W. M. P. van der Aalst. Activity mining by global trace segmentation. In BPM Workshops, pages 128--139, 2009.

[10]

C. W. Günther and W. M. P. van der Aalst. Mining activity clusters from low-level event logs,. In BETA Working Paper Series, volume 165. TUE, 2006.

[11]

C. W. Günther and W. M. P. van der Aalst. Fuzzy mining: adaptive process simplification based on multi-perspective metrics. In BPM, 2007.

Digital Library

[12]

H. Leopold, J. Mendling, H. A. Reijers, and M. L. Rosa. Simplifying process model abstraction: Techniques for generating model names. Information Systems, 39:134--151, Jan. 2014.

Digital Library

[13]

J. Li, R. Bose, and W. van der Aalst. Mining context-dependent and interactive business process maps using execution patterns. In BPM Workshops, 2011.

[14]

H. R. M. Nezhad, R. Saint-Paul, F. Casati, and B. Benatallah. Event correlation for process discovery from web service interaction logs. VLDB J., 20(3):417--444, 2011.

Digital Library

[15]

R. Pérez-Castillo, B. Weber, I. G.-R. de Guzmán, M. Piattini, and J. Pinggera. Assessing event correlation in non-process-aware information systems. Software and Systems Modeling, pages 1--23, 2012.

Digital Library

[16]

A. Polyvyanyy, L. García-Bañuelos, and M. Dumas. Structuring acyclic process models. Information Systems, 37(6):518--538, 2012.

Digital Library

[17]

A. Polyvyanyy, S. Smirnov, and M. Weske. Process Model Abstraction: A Slider Approach. In IEEE EDOC, pages 325--331, 2008.

Digital Library

[18]

A. Polyvyanyy, S. Smirnov, and M. Weske. On application of structural decomposition for process model abstraction. In BPSC, pages 110--122, 2009.

[19]

V. Popova, D. Fahland, and M. Dumas. Artifact lifecycle discovery. CoRR, abs/1303.2554, 2013.

[20]

S. Rozsnyai, A. Slominski, and G. T. Lakshmanan. Discovering event correlation rules for semi-structured business processes. In DEBS, pages 75--86, 2011.

Digital Library

[21]

S. Smirnov, H. A. Reijers, and M. Weske. From fine-grained to abstract process models: A semantic approach. Information Systems, 37(8):784--797, 2012.

Digital Library

[22]

S. Smirnov, M. Weidlich, and J. Mendling. Business process model abstraction based on synthesis from well-structured behavioral profiles. Intl. J. Cooperative Information Systems, 21(1):55--83, 2012.

[23]

M. Steinle, K. Aberer, S. Girdzijauskas, and C. Lovis. Mapping moving landscapes by mining mountains of logs: Novel techniques for dependency model generation. In VLDB, pages 1093--1102, 2006.

Digital Library

[24]

W. van der Aalst. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, 2011.

[25]

W. M. P. van der Aalst, A. H. M. ter Hofstede, B. Kiepuszewski, and A. P. Barros. Workflow patterns. Distributed & Parallel Databases, 14(1):5--51, 2003.

Digital Library

[26]

I. Weber, M. Farshchi, J. Mendling, and J.-G. Schneider. Mining processes with multi-instantiation: Discovery and conformance checking. Technical Report 201420, School of CSE, University of New South Wales, Sydney, Australia, Sept. 2014.

[27]

A. J. M. M. Weijters, W. M. P. van der Aalst, and A. K. Alves de Medeiros. Process mining with the heuristics miner-algorithm. In BETA Working Paper Series, volume 166. TUE, 2006.

[28]

X. Xu, I. Weber, H. Wada, L. Bass, L. Zhu, and S. Teng. Detecting cloud provisioning errors using an annotated process model. In Workshop on Middleware for Next Generation Internet Computing, 2013.

Digital Library

[29]

X. Xu, L. Zhu, I. Weber, L. Bass, and W. Sun. POD-diagnosis: Error diagnosis of sporadic operations on cloud applications. In IEEE/IFIP DSN, 2014.

Digital Library

Cited By

Liu CCheng LZeng QWen L(2023)Formal Modeling and Discovery of Hierarchical Business Processes: A Petri Net-Based ApproachIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2022.319586953:2(1003-1014)Online publication date: Feb-2023
https://doi.org/10.1109/TSMC.2022.3195869
Liu CWang YWen LCheng JCheng LZeng Q(2023)Discovering Hierarchical Multi-Instance Business Processes From Event LogsIEEE Transactions on Services Computing10.1109/TSC.2023.3335360(1-14)Online publication date: 2023
https://doi.org/10.1109/TSC.2023.3335360
Adams JPark Gvan der Aalst W(2023)Preserving complex object-centric graph structures to improve machine learning tasks in process miningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106764125:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106764
Show More Cited By

Index Terms

Mining processes with multi-instantiation
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation theory
      1. Systems theory
2. Mathematics of computing
  1. Information theory

Recommendations

Process Mining: Overview and Opportunities

Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/...
Use of Frequent Itemset Mining Techniques to Analyze Business Processes
IC3K 2015: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Analysis of business process data can be used to discover reasons of delays and other problems in a business

process. This paper presents an approach, which uses a simulator of production history. This simulator allows

detecting problems at various ...
Process diagnostics using trace alignment: Opportunities, issues, and challenges

Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National ICT Australia

Conference

SAC 2015

Sponsor:

SIGAPP

SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

Acceptance Rates

SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
191
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)1

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu CCheng LZeng QWen L(2023)Formal Modeling and Discovery of Hierarchical Business Processes: A Petri Net-Based ApproachIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2022.319586953:2(1003-1014)Online publication date: Feb-2023
https://doi.org/10.1109/TSMC.2022.3195869
Liu CWang YWen LCheng JCheng LZeng Q(2023)Discovering Hierarchical Multi-Instance Business Processes From Event LogsIEEE Transactions on Services Computing10.1109/TSC.2023.3335360(1-14)Online publication date: 2023
https://doi.org/10.1109/TSC.2023.3335360
Adams JPark Gvan der Aalst W(2023)Preserving complex object-centric graph structures to improve machine learning tasks in process miningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106764125:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106764
Liu C(2022)Formal Modeling and Discovery of Multi-instance Business Processes: A Cloud Resource Management Case StudyIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2022.1061099:12(2151-2160)Online publication date: Dec-2022
https://doi.org/10.1109/JAS.2022.106109
Birk AWilhelm YDreher SFlack CReimann PGröger C(2021)A Real-World Application of Process Mining for Data-Driven Analysis of Multi-Level Interlinked Manufacturing ProcessesProcedia CIRP10.1016/j.procir.2021.11.070104(417-422)Online publication date: 2021
https://doi.org/10.1016/j.procir.2021.11.070
Angelastro SFerilli S(2020)Process Model Modularization by Subprocess Discovery2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207229(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9207229
Martin NPufahl LMannhardt F(2020)Detection of batch activities from event logsInformation Systems10.1016/j.is.2020.101642(101642)Online publication date: Sep-2020
https://doi.org/10.1016/j.is.2020.101642
Conforti R(2019)Hierarchical Process DiscoveryEncyclopedia of Big Data Technologies10.1007/978-3-319-77525-8_94(942-947)Online publication date: 20-Feb-2019
https://doi.org/10.1007/978-3-319-77525-8_94
Jans MSoffer P(2018)From Relational Database to Event Log: Decisions with Quality ImpactBusiness Process Management Workshops10.1007/978-3-319-74030-0_46(588-599)Online publication date: 17-Jan-2018
https://doi.org/10.1007/978-3-319-74030-0_46
Conforti R(2018)Hierarchical Process DiscoveryEncyclopedia of Big Data Technologies10.1007/978-3-319-63962-8_94-1(1-7)Online publication date: 27-Mar-2018
https://doi.org/10.1007/978-3-319-63962-8_94-1
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents