International Conference on Database and Expert Systems Applications

DEXA 2015: Database and Expert Systems Applications pp 111-118 | Cite as

Detection of Sequences with Anomalous Behavior in a Workflow Process

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9261)

Abstract

A workflow process consists of an organized and repeatable pattern of activities that are necessary to complete a task, within the dynamics of an organization. The automatic recognition of deviations from the expected behavior within the workflow of an organization is crucial to provide assistance to new employees to accomplish his/her tasks. In this article, we propose a two-fold approach to this problem. First, taking the process logs as an input, we automatically build a statistical model that captures regularities in the activities carried out by the employees. Second, this model is used to track the activities performed by the employees to detect deviations from the expected behavior, according to the normal workflow of the organization. An experimental evaluation with five processes logs, with different levels of noise, was conducted to determine the validity of our approach.

Keywords

Process mining Outliers detection 

References

  1. 1.
    Kransdorff, A.: Corporate Amnesia: Keeping the Know-How in the Company. Butterworth Heinemann, Oxford (1998)MATHGoogle Scholar
  2. 2.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefMATHGoogle Scholar
  3. 3.
    Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM Trans. Softw. Eng. Methodol. 7, 215–249 (1998)CrossRefGoogle Scholar
  4. 4.
    Wen, L., Wang, J., Aalst, W.M., Huang, B., Sun, J.: A novel approach for process mining based on event types. J. Intell. Inf. Syst. 32, 163–190 (2009)CrossRefGoogle Scholar
  5. 5.
    Ghionna, L., Greco, G., Guzzo, A., Pontieri, L.: Outlier detection techniques for process mining applications. In: An, A., Matwin, S., Ras, Z., Slezak, D. (eds.) Foundations of Intelligent Systems. Lecture Notes in Computer Science, vol. 4994, pp. 150–159. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Chuang, Y.-C., Hsu, P.Y., Wang, M.T., Chen, S.-C.: A Frequency-Based Algorithm for Workflow Outlier Mining. In: Kim, T.-H., Lee, Y.-H., Kang, B.-H., Slezak, D. (eds.) FGIT 2010. LNCS, vol. 6485, pp. 191–207. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  7. 7.
    Bouarfa, L., Dankelman, J.: Workflow mining and outlier detection from clinical activity logs. J. Biomed. Inform. 45, 1185–1190 (2012)CrossRefMATHGoogle Scholar
  8. 8.
    Armentano, M., Amandi, A.: Modeling sequences of user actions for statistical goal recognition. User Model. User-Adap. Interact. 22, 281–311 (2012)CrossRefGoogle Scholar
  9. 9.
    Hunter, J.S.: The exponentially weighted moving average. J. Qual. Technol. 18, 203–209 (1986)Google Scholar
  10. 10.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data using little thumb. Integr. Comput.-Aided Eng. 10, 151–162 (2003)Google Scholar
  11. 11.
    Claes, J., Poels, G.: Merging computer log files for process mining: an artificial immune system technique. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 99–110. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  12. 12.
    Toon Jouck, B.D.: Generating artificial event logs to compare process discovery techniques. In: Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), vol. 1293. CEUR Workshop Proceedings, Milan, Italy (2014)Google Scholar
  13. 13.
    Burattin, A., Sperduti, A.: PLG: a framework for the generation of business process models and their execution logs. In: Muehlen, M., Su, J. (eds.) BPM 2010 Workshops. LNBIP, vol. 66, pp. 214–219. Springer, Heidelberg (2011) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.ISISTAN Research Institute (CONICET-UNICEN)Campus UniversitarioTandilArgentina

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