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A Rule Based Events Correlation Algorithm for Process Mining

  • Almir DjedovićEmail author
  • Almir Karabegović
  • Emir Žunić
  • Dino Alić
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 83)

Abstract

Process mining is a technique for extracting process models from event logs. Process mining can be used to discover, monitor and to improve real business processes by extracting knowledge from event logs available in process-aware information systems. This paper is concerned with the problem of grouping events in instances and the preparation of data for the process mining analysis. Often information systems do not store a unique identifier of the case instance, or errors happen in the system during the recording of events in the log files. To be able to analyze the process, it is necessary that events are grouped into case instances. The aim of the presented rule based algorithm is to find events belonging to the same case instance. Performances of the algorithm, for different sizes of log file events and different levels of errors within log files in the real process, have been analyzed.

References

  1. 1.
    Rozinat, A., Mans, R.S., Song, M., van der Aals, W.M.P.: Discovering simulation models. Inf. Syst. 34, 305–327 (2009).  https://doi.org/10.1016/j.is.2008.09.002CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., van Hee, K.M.: Workflow Management: Models, Methods and Systems. MIT Press, Cambridge (2004)Google Scholar
  3. 3.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., et al.: Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47, 237–267 (2003)CrossRefGoogle Scholar
  4. 4.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: 6th International Conference on Extending Database Technology. LNCS, vol. 1337, pp. 467–483 (1998).  https://doi.org/10.1007/bfb0101003Google Scholar
  5. 5.
    Grigori, D., Casati, F., Dayal, U., Sha, M.C.: Improving business process quality through exception understanding, prediction, and prevention. In: Proceedings of the 27th VLDB Conference, pp. 159–168 (2001)Google Scholar
  6. 6.
    Sayal, M., Casati, F., Dalay, U., Shan, M.C.: Business process cockpit. In: Proceedings of 28th International Conference on Very Large Data Bases (VLDB 2002), pp. 880–883 (2002)Google Scholar
  7. 7.
    Djedović, A., Žunić, E., Karabegović, A.: Model business process improvement by statistical analysis of the users’ conduct in the process. In: 2016 International Multidisciplinary Conference on Computer and Energy Science, pp. 1–6 (2016)Google Scholar
  8. 8.
    Joy, J., Rajeev, S., Narayanan, V.: Particle swarm optimization for resource constrained-project scheduling problem with varying resource levels. Proc. Technol. 25, 948–954 (2016)CrossRefGoogle Scholar
  9. 9.
    Huang, H., Ma, H., Zhang, M.: An enhanced genetic algorithm for web service location-allocation. LNCS, vol. 8645, pp. 223–230 (2014)Google Scholar
  10. 10.
    Park, J., Seo, D., Hong, G., et al.: Human resource allocation in software project with practical considerations. Int. J. Softw. Eng. Knowl. Eng. 25, 5–26 (2015).  https://doi.org/10.1142/S021819401540001XCrossRefGoogle Scholar
  11. 11.
    Djedović, A., Žunić, E., Avdagić, Z., Karabegović, A.: Optimization of business processes by automatic reallocation of resources using the genetic algorithm. In: XI International Symposium on Telecommunications – BIHTEL, pp. 1–7. IEEE (2016)Google Scholar
  12. 12.
    Tan, B., Ma, H., Zhang, M.: Optimization of location allocation of web services using a modified non-dominated sorting genetic algorithm. LNCS, vol. 9592, pp. 246–257 (2016)Google Scholar
  13. 13.
    van der Aalst, W.M.P., et al.: Process mining manifesto. In: Business Process Management Workshops. LNBIP, vol. 99, pp. 169–194 (2011)Google Scholar
  14. 14.
    Yimwadsana, B., Chaihirunkarn, C., Jaichoom, A., Thawornchak, A.: DocFlow: an integrated document workflow for business process management. Int. J. Digit. Inf. Wirel. Commun. (IJDIWC), 219–229 (2011)Google Scholar
  15. 15.
    Djedović, A., Žunić, E., Alić, D., Omanović, S., Karabegović, A.: Optimization of the business processes via automatic integration with the document management system. In: International Conference on Smart Systems and Technologies, pp. 117–122. IEEE (2016)Google Scholar
  16. 16.
    Burattin, A., Vigo, R.: A framework for semi-automated process instance discovery from decorative attributes. In: IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDM 2011: 2011 IEEE Symposium on Computational Intelligence and Data Mining, pp. 176–183 (2011)Google Scholar
  17. 17.
    Steinle, M., Aberer, K., et al.: Mapping moving landscapes by mining mountains of logs: novel techniques for dependency model generation 2000, pp. 1093–1102 (2000)Google Scholar
  18. 18.
    Günther, C.W., Rozinat, A., van Der Aalst, W.M.P.: Activity mining by global trace segmentation. LNBIP, pp. 128–139 (2010)Google Scholar
  19. 19.
    Li, J., Liu, D., Yang, B., Mining process models with duplicate tasks from workflow logs. LNCS, vol. 4537, pp. 396–407 (2007)Google Scholar
  20. 20.
    Bose, R.P.J.C., Verbeek, E.H.M.W., van Der Aalst, W.M.P.: Discovering hierarchical process models using ProM. In: CEUR Workshop Proceedings, pp. 33–40 (2011)Google Scholar
  21. 21.
    Walicki, M., Ferreira, D.R.: Mining sequences for patterns with non-repeating symbols. In: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 (2010)Google Scholar
  22. 22.
    Ferreira, D., Zacarias, M., Malheiros, M., Ferreira, P.: Approaching process mining with sequence clustering: experiments and findings. LNCS, vol. 4714, pp. 360–374 (2007).  https://doi.org/10.1007/978-3-540-75183-0_26Google Scholar
  23. 23.
    Perez-Castillo, R., Weber, B., et al.: Generating event logs from non-process-aware systems enabling business process mining. Enterp. Inf. Syst. 5, 301–335 (2011).  https://doi.org/10.1080/17517575.2011.587545CrossRefGoogle Scholar
  24. 24.
    Greco, G., Guzzo, A., Pontieri, L.: Mining taxonomies of process models. Data Knowl. Eng. 67, 74–102 (2008).  https://doi.org/10.1016/j.datak.2008.06.010CrossRefGoogle Scholar
  25. 25.
    Polyvyanyy, A., Smirnov, S., Weske, M.: Process model abstraction: a slider approach. In: Proceedings of the 12th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2008, pp. 325–331 (2008)Google Scholar
  26. 26.
    Bose, R.S.P.J.C., van Der Aalst, W.M.P.: Process diagnostics using trace alignment: opportunities, issues, and challenges. Inf. Syst. 37, 117–141 (2012).  https://doi.org/10.1016/j.is.2011.08.003CrossRefGoogle Scholar
  27. 27.
    Fahland, D., van Der Aalst, W.M.P.: Simplifying discovered process models in a controlled manner. Inf. Syst. 38, 585–605 (2013).  https://doi.org/10.1016/j.is.2012.07.004CrossRefGoogle Scholar
  28. 28.
    Baier, T., Di Ciccio, C., Mendling, J., Weske, M.: Matching events and activities by integrating behavioral aspects and label analysis. Softw. Syst. Model. 1–26 (2017)Google Scholar
  29. 29.
    Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Inf. Syst. 46, 123–139 (2014)CrossRefGoogle Scholar
  30. 30.
    Baier, T., Solti, A., Mendling, J., Weske, M.: Matching of events and activities - an approach based on behavioral constraint satisfaction. In: SAC, pp. 1225–1230. ACM (2015)Google Scholar
  31. 31.
    Mannhardt, F., de Leoni, M., Reijers, H.A., van der Aalst, W.M.P., Toussaint, P.J.: From low-level events to activities - a pattern-based approach. LNCS, vol. 9850, pp. 125–141 (2016)Google Scholar
  32. 32.
    Xu, Y., Lin, Q., Zhao, M.Q.: Merging event logs for process mining with hybrid artificial immune algorithm. In: International Conference on Data Mining, pp. 10–16 (2016)Google Scholar
  33. 33.
    Burke, E.K., Kendall, G.: Search Methodologies-Introductory Tutorials in Optimization and Decision Support Techniques, 2nd edn. Springer, New York (2014)zbMATHGoogle Scholar
  34. 34.
    Bayomie, D., Helal, I.M.A., Awad, A., Ezat, E., el Bastawissi, A.: Deducing case IDs for unlabeled event logs. In: BPI Workshop (2015)Google Scholar
  35. 35.
    Bayomie, D., Awad, A., Ezat, E.: Correlating unlabeled events from cyclic business processes execution. In: International Conference on Advanced Information Systems Engineering, pp. 274–289 (2016)Google Scholar
  36. 36.
    Claes, J., Poels, G.: Merging event logs for process mining: a rule based merging method and rule suggestion algorithm. Expert Syst. Appl. 41, 7291–7306 (2014)CrossRefGoogle Scholar
  37. 37.
    Process mining: Event logs. http://www.processmining.org/logs/start Accessed 10 Jan 2017

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Almir Djedović
    • 1
    Email author
  • Almir Karabegović
    • 1
  • Emir Žunić
    • 1
  • Dino Alić
    • 1
  1. 1.Faculty of Electrical EngineeringUniversity of SarajevoSarajevoBosnia and Herzegovina

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