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.
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Notes
- 1.
The resulting dataset is available online at: http://marcelo.armentano.isistan.unicen.edu.ar/datasets.
References
Kransdorff, A.: Corporate Amnesia: Keeping the Know-How in the Company. Butterworth Heinemann, Oxford (1998)
van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM Trans. Softw. Eng. Methodol. 7, 215–249 (1998)
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)
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)
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)
Bouarfa, L., Dankelman, J.: Workflow mining and outlier detection from clinical activity logs. J. Biomed. Inform. 45, 1185–1190 (2012)
Armentano, M., Amandi, A.: Modeling sequences of user actions for statistical goal recognition. User Model. User-Adap. Interact. 22, 281–311 (2012)
Hunter, J.S.: The exponentially weighted moving average. J. Qual. Technol. 18, 203–209 (1986)
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)
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)
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)
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)
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Armentano, M.G., Amandi, A.A. (2015). Detection of Sequences with Anomalous Behavior in a Workflow Process. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_8
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