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Experimenting with an OLAP Approach for Interactive Discovery in Process Mining

  • Gustavo Pizarro
  • Marcos SepúlvedaEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 202)

Abstract

Business process analysts must face the task of analyzing, monitoring and promoting improvements to different business processes. Process mining has emerged as a useful tool for analyzing event logs that are registered by information systems. It allows the discovering of process models considering different perspectives (control-flow, organizational, time). However, currently they lack the ability to explore jointly and interactively the different perspectives, which hinder the understanding of what is happening in the organization. This article proposes a novel approach for interactive discovery aimed at providing process analysts with a tool that allow them to explore multiple perspectives at different levels of detail, which is inspired on OLAP interactive concepts. This approach was implemented as a ProM plug-in and tested in an experiment with real users. Its main advantages are the productivity and operability when performing process discovery.

Keywords

Process mining Business process discovery OLAP 

References

  1. 1.
    Bandara, W., Chand, D.R., Chircu, A.M., Hintringer, S., Karagiannis, D., Recker, J.C., van Rensburg, A., Usoff, C., Welke, R.J.: Business process management education in academia: Status, challenges, and recommendations. Commun. Assoc. Inf. Syst. 27, 743–776 (2010)Google Scholar
  2. 2.
    Bayraktar, İ.: The Business Value of Process Mining Bringing It All Together. Eindhoven University of Technology, Eindhoven (2011)Google Scholar
  3. 3.
    Jagadeesh Chandra Bose, R.P., van der Aalst, W.: Trace alignment in process mining: Opportunities for process diagnostics. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 227–242. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Carmona, J.A., Cortadella, J., Kishinevsky, M.: A Region-Based Algorithm for Discovering Petri Nets from Event Logs. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 358–373. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Claes, J., Poels, G.: Process mining and the ProM framework: An exploratory survey. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 187–198. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Codd, E.F., Codd, S.B., Salley, C.T.: Providing OLAP (on-line analytical processing) to user-analysts: An IT mandate, vol. 32. Codd and Date (1993)Google Scholar
  7. 7.
    Doebeli, G., Fisher, R., Gapp, R., Sanzogni, L.: Using BPM governance to align systems and practice. Bus. Process Manage. J. 17(2), 184–202 (2011)CrossRefGoogle Scholar
  8. 8.
    Eckerson, W.W.: Performance Dashboards: Measuring, Monitoring, and Managing Your Business. Wiley, New York (2010)Google Scholar
  9. 9.
    Fluxicon Process Laboratories, Inc. [Download]: Disco version 1.5Google Scholar
  10. 10.
    Günther, C.W., van der Aalst, W.M.: Fuzzy mining – Adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    International Organization for Standardization: ISO 9126: Software Engineering – Product quality. Switzerland, Geneva (2001)Google Scholar
  12. 12.
    Mamaliga, T.: Realizing a process cube allowing for the comparison of event data. Master’s Thesis, Eindhoven University of Technology, Eindhoven (2013)Google Scholar
  13. 13.
    Mathiesen, P., Bandara, W., Delavari, H., Harmon, P., Brennan, K.: A comparative analysis of business analysis (BA) and business process management (BPM) capabilities. In: ECIS 2011 Proceedings (2011)Google Scholar
  14. 14.
    Newbold, P., Carlson, W., Thorne, B.: Statistics for Business and Economics. Pearson, New Jersey (2008)Google Scholar
  15. 15.
    Ribeiro, J.T.S.: Multidimensional Process Discovery. Eindhoven University of Technology, Eindhoven (2013)Google Scholar
  16. 16.
    van der Aalst, W.M.: Process Mining. Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  17. 17.
    Van der Aalst, W.M., Reijers, H.A., Song, M.: Discovering social networks from event logs. Comput. Support. Coop. Work (CSCW) 14(6), 549–593 (2005)CrossRefGoogle Scholar
  18. 18.
    van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011 Workshop, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  20. 20.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H., Weijters, A., van der Aalst, W.M.: The ProM framework: A new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  21. 21.
    Weijters, A.J.M.M., van der Aalst, W.M., De Medeiros, A.A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Technical Report, p. 166 (2006)Google Scholar
  22. 22.
    van der Aalst, W.M.: Process cubes: Slicing, dicing, rolling up and drilling down event data for process mining. In: Song, M., Wynn, M.T., Liu, J. (eds.) AP-BPM 2013. LNBIP, vol. 159, pp. 1–22. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Science Department, School of EngineeringPontificia Universidad Católica de ChileSantiagoChile

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