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Anomaly Detection in Business Process based on Data Stream Mining

Published: 04 June 2018 Publication History

Abstract

Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nevertheless, the continuous nature of business conveys to the continuous acquisition of data in support of business process monitoring. In light of this, we propose a method for online anomaly detection in business processes. From a stream of events, our approach extract cases descriptors and applies a density-based clustering technique to detect outliers. We applied our method to a real-life dataset, and we used streaming clustering measures for evaluating performances. In particular, we obtained Cluster Mapping Measure of 95.3% and Homogeneity of 98.1% discovering anomalous cases in real-time.

References

[1]
S. Barbon, G. M. Tavares, V. G. T. da Costa, P. Ceravolo, and E. Damiani. A framework for human-in-the-loop monitoring of concept-drift detection in event log stream. In WWW '18 Companion: The 2018 Web Conference Companion, April 23-27, 2018, Lyon, France. ACM, 2018.
[2]
T. Becker and W. Intoyoad. Context aware process mining in logistics. Procedia CIRP, 63:557--562, 2017. Manufacturing Systems 4.0 -- Proceedings of the 50th CIRP Conference on Manufacturing Systems.
[3]
A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer. Moa: Massive online analysis. Journal of Machine Learning Research, 11(May):1601--1604, 2010.
[4]
K. Böhmer and S. Rinderle-Ma. Anomaly detection in business process runtime behavior--challenges and limitations. arXiv preprint arXiv:1705.06659, 2017.
[5]
R. J. C. Bose and W. M. van der Aalst. Trace alignment in process mining: Opportunities for process diagnostics. In BPM, volume 6336, pages 227--242. Springer, 2010.
[6]
F. Cao, M. Estert, W. Qian, and A. Zhou. Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining, pages 328--339. SIAM, 2006.
[7]
J. Carmona and J. Cortadella. Process mining meets abstract interpretation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 184--199. Springer, 2010.
[8]
P. Ceravolo, E. Damiani, M. Torabi, and S. Barbon. Toward a New Generation of Log Pre-processing Methods for Process Mining, pages 55--70. Springer International Publishing, Cham, 2017.
[9]
P. Domingos and G. Hulten. Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 71--80. ACM, 2000.
[10]
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD'96, pages 226--231. AAAI Press, 1996.
[11]
M. Frigge, D. C. Hoaglin, and B. Iglewicz. Some implementations of the boxplot. The American Statistician, 43(1):50--54, 1989.
[12]
G. L. Gray and R. S. Debreceny. A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4):357--380, 2014.
[13]
M. Jans, J. M. van der Werf, N. Lybaert, and K. Vanhoof. A business process mining application for internal transaction fraud mitigation. Expert Systems with Applications, 38(10):13351--13359, 2011.
[14]
L. Juhaňák, J. Zounek, and L. Rohlíková. Using process mining to analyze students' quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 2017.
[15]
T. Kilpeläinen and P. Tyrväinen. The degree of digitalization of the information over-flow: A case study. In ICEIS 2004, Proceedings of the 6th International Conference on Enterprise Information Systems, Porto, Portugal, April 14-17, 2004, pages 367--374, 2004.
[16]
H. Kremer, P. Kranen, T. Jansen, T. Seidl, A. Bifet, G. Holmes, and B. Pfahringer. An effective evaluation measure for clustering on evolving data streams. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 868--876. ACM, 2011.
[17]
S. J. J. Leemans, D. Fahland, and W. M. P. van der Aalst. Discovering block-structured process models from event logs containing infrequent behaviour. In N. Lohmann, M. Song, and P. Wohed, editors, Business Process Management Workshops, pages 66--78, Cham, 2014. Springer International Publishing.
[18]
L. Lévesque. Nyquist sampling theorem: understanding the illusion of a spinning wheel captured with a video camera. Physics Education, 49(6):697--705, 2014.
[19]
T. Murata. Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4):541--580, Apr 1989.
[20]
E. Ngai, Y. Hu, Y. Wong, Y. Chen, and X. Sun. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3):559--569, 2011.
[21]
W. Reisig. Petri Nets: An Introduction, volume 4 of EATCS Monographs on Theoretical Computer Science. Springer, 1985.
[22]
A. Rosenberg and J. Hirschberg. V-measure: A conditional entropy-based external cluster evaluation measure. In EMNLP-CoNLL, volume 7, pages 410--420, 2007.
[23]
W. M. P. van der Aalst. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer Publishing Company, Incorporated, 1st edition, 2011.
[24]
R. A. Wagner and M. J. Fischer. The string-to-string correction problem. J. ACM, 21(1):168--173, Jan. 1974.
[25]
J. Wang, R. K. Wong, J. Ding, Q. Guo, and L. Wen. On recommendation of process mining algorithms. In Web Services (ICWS), 2012 IEEE 19th International Conference on, pages 311--318. IEEE, 2012.
[26]
J. West and M. Bhattacharya. Intelligent financial fraud detection: a comprehensive review. Computers & Security, 57:47--66, 2016.
[27]
I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
[28]
W.-S. Yang and S.-Y. Hwang. A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1):56--68, 2006.

Cited By

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  • (2025)Survey and Benchmark of Anomaly Detection in Business ProcessesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348415937:1(493-512)Online publication date: Jan-2025
  • (2024)Metaheuristic and Data Mining Algorithms-based Feature Selection Approach for Anomaly DetectionIETE Journal of Research10.1080/03772063.2023.229967370:7(6040-6054)Online publication date: 15-Jan-2024
  • (2023)CEMDAACM SIGAPP Applied Computing Review10.1145/3584014.358401622:4(24-36)Online publication date: 10-Feb-2023
  • Show More Cited By

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cover image ACM Other conferences
SBSI '18: Proceedings of the XIV Brazilian Symposium on Information Systems
June 2018
578 pages
ISBN:9781450365598
DOI:10.1145/3229345
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2018

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Author Tags

  1. Business Process Modelling
  2. Clustering
  3. Fraud
  4. Online
  5. Process Mining

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  • Research-article
  • Research
  • Refereed limited

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SBSI'18
SBSI'18: XIV Brazilian Symposium on Information Systems
June 4 - 8, 2018
Caxias do Sul, Brazil

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Overall Acceptance Rate 181 of 557 submissions, 32%

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Cited By

View all
  • (2025)Survey and Benchmark of Anomaly Detection in Business ProcessesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348415937:1(493-512)Online publication date: Jan-2025
  • (2024)Metaheuristic and Data Mining Algorithms-based Feature Selection Approach for Anomaly DetectionIETE Journal of Research10.1080/03772063.2023.229967370:7(6040-6054)Online publication date: 15-Jan-2024
  • (2023)CEMDAACM SIGAPP Applied Computing Review10.1145/3584014.358401622:4(24-36)Online publication date: 10-Feb-2023
  • (2022)Anomalous Behavior Detection Based on the Isolation Forest Model with Multiple Perspective Business ProcessesElectronics10.3390/electronics1121364011:21(3640)Online publication date: 7-Nov-2022
  • (2022)Evaluation Goals for Online Process Mining: A Concept Drift PerspectiveIEEE Transactions on Services Computing10.1109/TSC.2020.300453215:4(2473-2489)Online publication date: 1-Jul-2022
  • (2022)Online incremental updating for model enhancement based on multi-perspective trusted intervalsConnection Science10.1080/09540091.2022.208869634:1(1956-1980)Online publication date: 21-Jun-2022
  • (2021)On the use of online clustering for anomaly detection in trace streamsProceedings of the XVII Brazilian Symposium on Information Systems10.1145/3466933.3466979(1-8)Online publication date: 7-Jun-2021
  • (2021)A Real-Time Method for Detecting Temporary Process Variants in Event Log DataBusiness Process Management10.1007/978-3-030-85469-0_14(197-214)Online publication date: 28-Aug-2021
  • (2019)Comparing Concept Drift Detection with Process Mining ToolsProceedings of the XV Brazilian Symposium on Information Systems10.1145/3330204.3330240(1-8)Online publication date: 20-May-2019

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