research-article

Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering

Authors:

Rafael Gaspar de Sousa,

Sarajane Marques Peres,

Marcelo Fantinato,

Hajo Alexander ReijersAuthors Info & Claims

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

Pages 364 - 373

https://doi.org/10.1145/3412841.3441918

Published: 22 April 2021 Publication History

Abstract

Business processes are subject to changes over time due to the need for adaptation and flexibility to a complex environment. Detecting drift as soon as possible and identifying the process elements involved, lead to a much better understanding of the process behavior, which can be a competitive edge for businesses. However, most existing approaches focus on each of these two tasks separately. Isolated approaches do not always have interfaces between them that allow you to combine solutions effectively for each corresponding task. In such cases, using the two isolated solutions together is neither feasible nor even useful from the point of view of a business analyst. This paper proposes an integrated approach to detect and locate concept drifts based on an online setting for trace clustering. Experiments with synthetic event logs with different types of control-flow changes showed that concept drifts can be detected and located efficiently.

References

[1]

Annalisa Appice and Donato Malerba. 2016. A Co-Training Strategy for Multiple View Clustering in Process Mining. IEEE Trans. Serv. Comput. 9, 6 (2016), 832--845.

[2]

Albert Bifet and Ricard Gavaldá. 2007. Learning from Time-Changing Data with Adaptive Windowing. Proceedings of the 7th SIAM International Conference on Data Mining 7.

[3]

Alfredo Bolt, Massimiliano de Leoni, and Wil van der Aalst. 2018. Process variant comparison: Using event logs to detect differences in behavior and business rules. Inf. Syst. 74 (2018), 53 -- 66.

Digital Library

[4]

R. P. Jagadeesh Chandra Bose, Wil van der Aalst, Indre Žliobaité, and Mykola Pechenizkiy. 2011. Handling Concept Drift in Process Mining. In 23rd Int. Conf. on Adv. Inf. Syst. Eng. 391--405.

[5]

Josep Carmona and Ricard Gavaldá. 2012. Online Techniques for Dealing with Concept Drift in Process Mining. In 11th Int. Symp. Adv. in Intel. Data Anal. 90--102.

[6]

Matthias Carnein and Heike Trautmann. 2019. Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. Bus. & Inf. Syst. Eng. 61 (2019), 277--297.

[7]

Cleiton dos S. Garcia, Alex Meincheim, Elio R. Faria Junior, Marcelo R. Dallagassa, Denise Maria V. Sato, Deborah R. Carvalho, Eduardo A. P. Santos, and Edson Emilio Scalabrin. 2019. Process mining techniques and applications - A systematic mapping study. Expert Syst. Appl. 133 (2019), 260 -- 295.

Digital Library

[8]

Marlon Dumas, Marcello La Rosa, Jan Mendling, and Hajo A. Reijers. 2013. Fundamentals of Business Process Management.

Digital Library

[9]

Angelo Impedovo, Paolo Mignone, Corrado Loglisci, and Michelangelo Ceci. 2020. Simultaneous Process Drift Detection and Characterization with Pattern-Based Change Detectors. In Discovery Science. Springer International Publishing, Cham, 451--467.

[10]

Paulo M. Gonçalves Jr., Silas G.T. de Carvalho Santos, Roberto S.M. Barros, and Davi C.L. Vieira. 2014. A comparative study on concept drift detectors. Expert Syst. Appl. 41, 18 (2014), 8144 -- 8156.

[11]

Harold William Kuhn. 1955. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 1--2 (1955), 83--97.

[12]

Na Liu, Jiwei Huang, and Lizhen Cui. 2018. A Framework for Online Process Concept Drift Detection from Event Streams. In IEEE Int. Conf. on Serv. Comput. 105--112.

[13]

Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa, and Alireza Ostovar. 2015. Business Process Drift. Dataset, Queensland Univ. of Technology, Australia.

[14]

Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa, and Alireza Ostovar. 2015. Fast and Accurate Business Process Drift Detection. In Bus. Process Manage. 406--422.

[15]

Ana Rocío C. Maita, Lucas C. Martins, Carlos Ramón L. Paz, Laura Rafferty, Patrick C. K. Hung, Sarajane M. Peres, and Marcelo Fantinato. 2017. A systematic mapping study of process mining. Enterprise Inf. Sys. (2017).

[16]

Hoang Nguyen, Marlon Dumas, Marcello La Rosa, and Arthur H. M. ter Hofstede. 2018. Multi-perspective Comparison of Business Process Variants Based on Event Logs. In Conceptual Modeling. 449--459.

[17]

Márcia Oliveira and João Gama. 2010. MEC - Monitoring Clusters' Transitions. In 5th Starting AI Researchers' Symp. 212--224.

[18]

Alireza Ostovar, Sander J. J. Leemans, and Marcello La Rosa. 2020. Robust Drift Characterization from Event Streams of Business Processes. ACM Trans. Knowl. Discov. Data 14, 3, Article 30 (2020), 57 pages.

Digital Library

[19]

Alireza Ostovar, Abderrahmane Maaradji, Marcello La Rosa, and Arthur ter Hofstede. 2017. Characterizing drift from event streams of business processes. In 29th Int. Conf. on Adv. Inf. Syst. Eng. 210--228.

[20]

Alireza Ostovar, Abderrahmane Maaradji, Marcello La Rosa, Arthur H.M. ter Hofstede, and Boudewijn van Dongen. 2016. Detecting drift from event streams of unpredictable business processes. In 35th Int. Conf. on Conceptual Model.

[21]

Alexander Seeliger, Timo Nolle, and Max Mühlhäuser. 2017. Detecting Concept Drift in Processes Using Graph Metrics on Process Graphs. In 9th Conf. on Subject-Oriented Bus. Process Manage. Article 6, 10 pages.

[22]

Minseok Song, Christian W. Günther, and Wil van der Aalst. 2008. Trace clustering in process mining. In 4th Workshop on Bus. Process Intell. 51--62.

[23]

Wil van der Aalst. 2011. Process Mining: Discovery, Conformance and Enhancement of Business Processes (1st ed.). 352 pages.

[24]

Wil van der Aalst. 2016. Process Mining: Data Science in Action (2nd ed.).

[25]

Geoffrey I. Webb, Roy Hyde, Hong Cao, Hai L. Nguyen, and Francois Petitjean. 2016. Characterizing Concept Drift. Data Min. Knowl. Discov. 30, 4 (2016), 964--994.

Digital Library

[26]

Indre Žliobaité, Mykola Pechenizkiy, and João Gama. 2016. An Overview of Concept Drift Applications. In Big Data Analysis. Vol. 16. 91--114.

Cited By

Maita AFantinato MPeres SMaggi F(2024)Supporting Interpretability in Predictive Process Monitoring Using Process MapsEnterprise Information Systems10.1007/978-3-031-64748-2_11(230-246)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-3-031-64748-2_11
Neubauer TPeeperkorn JPeres SDe Weerdt JFantinato M(2023)Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00087(588-594)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00087
Guan WCao JGu YQian S(2023)AIMEDInformation Systems10.1016/j.is.2023.102285119:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102285
Show More Cited By

Index Terms

Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering
1. Applied computing
  1. Enterprise computing
    1. Business process management
      1. Business process monitoring
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
      2. Data stream mining

Recommendations

A Survey on Concept Drift in Process Mining

Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we ...
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/...
Integrated detection and localization of concept drifts in process mining with batch and stream trace clustering support
Abstract
Process mining can help organizations by extracting knowledge from event logs. However, process mining techniques often assume business processes are stationary, while actual business processes are constantly subject to change because of the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

March 2021

2075 pages

ISBN:9781450381048

DOI:10.1145/3412841

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Jiman Hong
Soongsil University, South Korea
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Eunjee Song
Baylor University

Copyright © 2021 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: 22 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC '21

Sponsor:

SIGAPP

SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing

March 22 - 26, 2021

Virtual Event, Republic of Korea

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
296
Total Downloads

Downloads (Last 12 months)58
Downloads (Last 6 weeks)6

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Maita AFantinato MPeres SMaggi F(2024)Supporting Interpretability in Predictive Process Monitoring Using Process MapsEnterprise Information Systems10.1007/978-3-031-64748-2_11(230-246)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-3-031-64748-2_11
Neubauer TPeeperkorn JPeres SDe Weerdt JFantinato M(2023)Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00087(588-594)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00087
Guan WCao JGu YQian S(2023)AIMEDInformation Systems10.1016/j.is.2023.102285119:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102285
Hanga KKovalchuk YGaber M(2022)PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business ProcessesEntropy10.3390/e2407091024:7(910)Online publication date: 30-Jun-2022
https://doi.org/10.3390/e24070910
Bresso EFerreira JGirerd NKobayashi MPreud’homme GRossignol PZannad FDevignes MSmaïl-Tabbone M(2022)Inductive database to support iterative data miningJournal of Biomedical Informatics10.1016/j.jbi.2022.104212135:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.jbi.2022.104212
Oberdorf FSchaschek MWeinzierl SStein NMatzner MFlath C(2022)Predictive End-to-End Enterprise Process Network MonitoringBusiness & Information Systems Engineering10.1007/s12599-022-00778-465:1(49-64)Online publication date: 6-Dec-2022
https://doi.org/10.1007/s12599-022-00778-4
Xu JZhang YDuan Q(2022)Concept drift detection and localization framework based on behavior replacementApplied Intelligence10.1007/s10489-022-04341-253:13(16776-16796)Online publication date: 16-Dec-2022
https://dl.acm.org/doi/10.1007/s10489-022-04341-2
Neubauer TPamponet Sobrinho GFantinato MPeres S(2021)Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671985(3548-3556)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671985

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