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.
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References
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)
Bayraktar, İ.: The Business Value of Process Mining Bringing It All Together. Eindhoven University of Technology, Eindhoven (2011)
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)
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)
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)
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)
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)
Eckerson, W.W.: Performance Dashboards: Measuring, Monitoring, and Managing Your Business. Wiley, New York (2010)
Fluxicon Process Laboratories, Inc. [Download]: Disco version 1.5
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)
International Organization for Standardization: ISO 9126: Software Engineering – Product quality. Switzerland, Geneva (2001)
Mamaliga, T.: Realizing a process cube allowing for the comparison of event data. Master’s Thesis, Eindhoven University of Technology, Eindhoven (2013)
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)
Newbold, P., Carlson, W., Thorne, B.: Statistics for Business and Economics. Pearson, New Jersey (2008)
Ribeiro, J.T.S.: Multidimensional Process Discovery. Eindhoven University of Technology, Eindhoven (2013)
van der Aalst, W.M.: Process Mining. Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
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)
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)
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)
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)
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)
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)
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Pizarro, G., Sepúlveda, M. (2015). Experimenting with an OLAP Approach for Interactive Discovery in Process Mining. In: Fournier, F., Mendling, J. (eds) Business Process Management Workshops. BPM 2014. Lecture Notes in Business Information Processing, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-319-15895-2_27
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DOI: https://doi.org/10.1007/978-3-319-15895-2_27
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