Advertisement

A Business Process Metric Based on the Alpha Algorithm Relations

  • Fabio Aiolli
  • Andrea Burattin
  • Alessandro Sperduti
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 99)

Abstract

We present a metric for the comparison of business process models. This new metric is based on a representation of a given model as two sets of local relations between pairs of activities in the model. In order to build this two sets, the same relations defined for the Alpha Algorithm [2] are considered. The proposed metric is then applied to hierarchical clustering of business process models and the whole procedure is implemented and made publicly available.

Keywords

Business Process Dependency Graph Business Process Model Causal Dependency Primitive Relation 
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.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., van Dongen, B.F.: Discovering Workflow Performance Models from Timed Logs. In: Han, Y., Tai, S., Wikarski, D. (eds.) EDCIS 2002. LNCS, vol. 2480, pp. 45–63. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.: Process Equivalence: Comparing Two Process Models Based on Observed Behavior. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 129–144. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Aiolli, F., Burattin, A., Sperduti, A.: A Metric for Clustering Business Processes Based on Alpha Algorithm Relations. Tech. rep (2011), http://www.processmining.it
  5. 5.
    Bae, J., Liu, L., Caverlee, J., Zhang, L.-J., Bae, H.: Development of Distance Measures for Process Mining, Discovery, and Integration. International Journal of Web Services Research 4(4), 1–17 (2007)CrossRefGoogle Scholar
  6. 6.
    Burattin, A., Sperduti, A.: Automatic determination of parameters’ values for Heuristics Miner++. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE, Barcelona (2010)CrossRefGoogle Scholar
  7. 7.
    Burattin, A., Sperduti, A.: PLG: A Framework for the Generation of Business Process Models and Their Execution Logs. In: zur Muehlen, M., Su, J. (eds.) BPM 2010 Workshops. LNBIP, vol. 66, pp. 214–219. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Dijkman, R.: Diagnosing Differences between Business Process Models. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 261–277. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    van Dongen, B.F., Dijkman, R., Mendling, J.: Measuring Similarity between Business Process Models. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 450–464. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Ehrig, M., Koschmider, A., Oberweis, A.: Measuring Similarity between Semantic Business Process Models. In: Proceedings of the Fourth Asia-Pacific Conference on Conceptual Modelling, pp. 71–80 (2007)Google Scholar
  11. 11.
    Măruşter, L., Weijters, A.J.M.M., van der Aalst, W.M.P., van den Bosch, A.: Process Mining: Discovering Direct Successors in Process Logs. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 364–373. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    de Medeiros, A.K.A., van der Aalst, W.M.P., Weijters, A.J.M.M.: Quantifying process equivalence based on observed behavior. Data & Knowledge Engineering 64(1), 55–74 (2008)CrossRefGoogle Scholar
  13. 13.
    Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets (2010)Google Scholar
  14. 14.
    Russell, N., Ter Hofstede, A.H.M., van der Aalst, W.M.P., Mulyar, N.: Workflow control-flow patterns: A revised view. BPM Center Report BPM-06-22, BPMcenter. org (2006)Google Scholar
  15. 15.
    Wang, J., He, T., Wen, L., Wu, N., ter Hofstede, A.H.M., Su, J.: A Behavioral Similarity Measure between Labeled Petri Nets Based on Principal Transition Sequences. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 394–401. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Weidlich, M., Mendling, J., Weske, M.: Efficient Consistency Measurement Based on Behavioral Profiles of Process Models. IEEE Transactions on Software Engineering 37(3), 410–429 (2011)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zha, H., Wang, J., Wen, L., Wang, C., Sun, J.: A workflow net similarity measure based on transition adjacency relations. Comp. in Industry 61(5), 463–471 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fabio Aiolli
    • 1
  • Andrea Burattin
    • 1
  • Alessandro Sperduti
    • 1
  1. 1.Department of Pure and Applied MathematicsUniversity of PaduaItaly

Personalised recommendations