Abstract
Business processes are traditionally regarded as generalized abstractions describing the activities and common behaviour of a large group of process instances. However, the recent developments in process mining and data analysis show that individual process instances may behave very different from each other. In this paper we present a generic methodology called influence analysis for finding business improvement areas related to business processes. Influence analysis is based on process mining, root cause analysis and classification rule mining. We present three generic target levels for business improvements and define corresponding probability-based interestingness measures. We then define measures for reporting the contribution results to business people and show how these measures can be used to focus improvements. Real-life case study is also included to show the methodology in action.
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References
Andersen, B., Fagerhaug, T.: Root Cause Analysis: Simplified Tools and Techniques. ASQ Quality Press, Milwaukee (2006)
Bay, S., Pazzani, M.: Detecting group differences: mining contrast sets. Data Min. Knowl. Disc. 5(3), 213–246 (2001)
de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general framework for correlating business process characteristics. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 250–266. Springer, Heidelberg (2014)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. (CSUR) 38(3), 9 (2006)
Goldratt, E.M.: Theory of Constraints. North River, Croton-on-Hudson (1990)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)
Inmon, W.H.: Building the Data Warehouse. Wiley, New York (2005)
Kakas, A.C., Kowalski, R.A., Toni, F.: Abductive logic programming. J. Logic Comput. 2(6), 719–770 (1992)
Mayer-Schnberger, V., Cukier, K.: Big Data: A Revolution that Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, Boston (2013)
Pearl, J.: Causality: Models, Reasoning and Inference, vol. 29. MIT Press, Cambridge (2000)
Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248 (1991)
QPR Software Plc.: QPR Software to Offer Business Process optimization with Automated Business Process Discovery Software QPR Process Analyzer, Press release 15 Feb 2011
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Rozinat, A., van der Aalst, W.M.P.: Decision Mining in ProM. Springer, Heidelberg (2006)
Suriadi, S., Ouyang, C., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Root cause analysis with enriched process logs. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 174–186. Springer, Heidelberg (2013)
van der Aalst, W.M.P., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)
van der Aalst, W.M.P., Adriansyah, A., van Dongen, B.: Causal nets: a modeling language tailored towards process discovery. In: Katoen, J.-P., König, B. (eds.) CONCUR 2011. LNCS, vol. 6901, pp. 28–42. Springer, Heidelberg (2011)
Van Dongen, B.F.: BPI Challenge 2014. Rabobank Nederland. Dataset (2014). http://dx.doi.org/10.4121/uuid:c3e5d162-0cfd-4bb0-bd82-af5268819c35
Webb, G.I., Butler, S., Newlands, D.: On detecting differences between groups. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003 (2003)
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We thank QPR Software Plc for the practical experiences from a wide variety of customer cases and for funding our research.
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Lehto, T., Hinkka, M., Hollmén, J. (2016). Focusing Business Improvements Using Process Mining Based Influence Analysis. In: La Rosa, M., Loos, P., Pastor, O. (eds) Business Process Management Forum. BPM 2016. Lecture Notes in Business Information Processing, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-319-45468-9_11
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DOI: https://doi.org/10.1007/978-3-319-45468-9_11
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