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Directed Acyclic Graph Extraction from Event Logs

  • Olegas Vasilecas
  • Titas Savickas
  • Evaldas Lebedys
Part of the Communications in Computer and Information Science book series (CCIS, volume 465)

Abstract

The usage of probabilistic models in business process mining enables analysis of business processes in a more efficient manner. Although, the Bayesian belief network is one of the most common probabilistic models, possibilities to use it in business process mining are still not widely researched. Existing process mining approaches are incapable to extract directed acyclic graphs for representing Bayesian networks. This paper presents an approach for extraction of directed acyclic graph from event logs. The results obtained during the experiment show that the proposed approach is feasible and may be applied in practice.

Keywords

Process mining direct acyclic graph event log Bayesian belief network 

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References

  1. 1.
    Aytulun, S.K., Guneri, A.F.: Business process modelling with stochastic networks. Int. J. Prod. Res. 46, 2743–2764 (2008)CrossRefzbMATHGoogle Scholar
  2. 2.
    Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Inf. Syst. 34, 305–327 (2009)CrossRefGoogle Scholar
  3. 3.
    van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle time prediction: When will this case finally be finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)Google Scholar
  4. 4.
    van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.: Genetic process mining. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 48–69. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Van Dongen, B.F., van der Aalst, W.M.P.: A Meta Model for Process Mining Data. EMOI-INTEROP 160, 30 (2005)Google Scholar
  6. 6.
    Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: XES, xESame, and proM 6. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 60–75. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9, 87–111 (2010)CrossRefGoogle Scholar
  8. 8.
    Weijters, A.J.M.M., Van Der Aalst, W.M.P., Medeiros, A.K.A.: De: Process Mining with the HeuristicsMiner Algorithm. Cirp Ann. Technol. 166, 1–34 (2006)Google Scholar
  9. 9.
    van Dongen, B.F., Alves de Medeiros, A.K., Wen, L.: Process mining: Overview and outlook of petri net discovery algorithms. In: Jensen, K., van der Aalst, W.M.P. (eds.) ToPNoC II. LNCS, vol. 5460, pp. 225–242. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Mannila, H., Meek, C.: Global partial orders from sequential data. In: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 161–168 (2000)Google Scholar
  11. 11.
    van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle time prediction: When will this case finally be finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)Google Scholar
  12. 12.
    Van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36, 450–475 (2011)CrossRefGoogle Scholar
  13. 13.
    Liu, Y., Zhang, H., Li, C., Jiao, R.J.: Workflow simulation for operational decision support using event graph through process mining. Decis. Support Syst. 52, 685–697 (2012)CrossRefGoogle Scholar
  14. 14.
    Rozinat, A., Van Der Aalst, W.M.P.: Decision Mining in Business ProcessesGoogle Scholar
  15. 15.
    De Leoni, M., van der Aalst, W.M.P.: Data-aware process mining: discovering decisions in processes using alignments. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 1454–1461 (2013)Google Scholar
  16. 16.
    Sutrisnowati, R.A., Bae, H., Park, J., Ha, B.-H.: Learning Bayesian Network from Event Logs Using Mutual Information Test. In: 2013 IEEE 6th Int. Conf. Serv. Comput. Appl., pp. 356–360 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Olegas Vasilecas
    • 1
  • Titas Savickas
    • 1
  • Evaldas Lebedys
    • 1
  1. 1.Information Systems Research LaboraryVilnius Gediminas Technical UniversityLithuania

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