Advertisement

Abstractions in Process Mining: A Taxonomy of Patterns

  • R. P. Jagadeesh Chandra Bose
  • Wil M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5701)

Abstract

Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. One reason for such a result can be attributed to constructing process models from raw traces without due pre-processing. In an event log, there can be instances where the system is subjected to similar execution patterns/behavior. Discovery of common patterns of invocation of activities in traces (beyond the immediate succession relation) can help in improving the discovery of process models and can assist in defining the conceptual relationship between the tasks/activities.

In this paper, we characterize and explore the manifestation of commonly used process model constructs in the event log and adopt pattern definitions that capture these manifestations, and propose a means to form abstractions over these patterns. We also propose an iterative method of transformation of traces which can be applied as a pre-processing step for most of today’s process mining techniques. The proposed approaches are shown to identify promising patterns and conceptually-valid abstractions on a real-life log. The patterns discussed in this paper have multiple applications such as trace clustering, fault diagnosis/anomaly detection besides being an enabler for hierarchical process discovery.

Keywords

Maximal Element Edit Distance Tandem Array Abstract Entity Process Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van der Aalst, W., Weijters, A., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  2. 2.
    de Medeiros, A.K.A., van der Aalst, W., Pedrinaci, C.: Semantic Process Mining Tools: Core Building Blocks. In: 16th European Conference on Information Systems, pp. 1953–1964 (2008)Google Scholar
  3. 3.
    Ristad, E.S., Yianilos, P.N.: Learning String-Edit Distance. IEEE Trans. PAMI 20(5), 522–532 (1998)CrossRefGoogle Scholar
  4. 4.
    Bose, R.P.J.C., van der Aalst, W.: Context Aware Trace Clustering: Towards Improving Process Mining Results. In: SIAM International Conference on Data Mining, pp. 401–412 (2009)Google Scholar
  5. 5.
    Gusfield, D.: Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press, Cambridge (1997)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kolpakov, K.: Finding Maximal Repetitions in a Word in Linear Time. In: IEEE Symposium on Foundations of Computer Science (FOCS), pp. 596–604 (1999)Google Scholar
  7. 7.
    Cheung, C.F., Yu, J.X., Lu, H.: Constructing Suffix Tree for Gigabyte Sequences with Megabyte Memory. IEEE Trans. Knowl. Data Eng. 17(1), 90–105 (2005)CrossRefGoogle Scholar
  8. 8.
    Gusfield, D., Stoye, J.: Linear Time Algorithms for Finding and Representing all the Tandem Repeats in a String. Journal of Computer and System Sciences 69, 525–546 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Sokol, D., Benson, G., Tojeira, J.: Tandem Repeats Over the Edit Distance. Bioinformatics 23(2), e30–e36 (2007)CrossRefGoogle Scholar
  10. 10.
    Ukkonen, E.: On-Line Construction of Suffix Trees. Algorithmica 14(3), 249–260 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Greco, G., Guzzo, A., Pontieri, L.: Mining Hierarchies of Models: From Abstract Views to Concrete Specifications. In: Business Process Management, pp. 32–47 (2005)Google Scholar
  12. 12.
    Greco, G., Guzzo, A., Pontieri, L.: Mining Taxonomies of Process Models. Data Knowl. Eng. 67(1), 74–102 (2008)CrossRefGoogle Scholar
  13. 13.
    Polyvyanyy, A., Smirnov, S., Weske, M.: Process Model Abstraction: A Slider Approach. In: Enterprise Distributed Object Computing, pp. 325–331 (2008)Google Scholar
  14. 14.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics. In: Business Process Management, pp. 328–343 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • R. P. Jagadeesh Chandra Bose
    • 1
    • 2
  • Wil M. P. van der Aalst
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of TechnologyEindhovenThe Netherlands
  2. 2.Philips HealthcareBestThe Netherlands

Personalised recommendations