Abstract
The organizational perspective of process mining supports the discovery of social networks within organizations by analyzing event logs recorded during process execution. However, applying these social network mining techniques to real data generates very complex models that are hard to analyze and understand. In this work we present an approach to overcome these difficulties by focusing on the discovery of communities from such event logs. The clustering of users into communities allows the analysis and visualization of the social network at different levels of abstraction. The proposed approach also makes use of the concept of modularity, which provides an indication of the best division of the social network into community clusters. The approach was implemented in the ProM framework and it was successfully applied in the analysis of the emergency service of a medium-sized hospital.
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van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: A survey of issues and approaches. Data and Knowledge Engineering 47(2), 237–267 (2003)
Tiwari, A., Turner, C., Majeed, B.: A review of business process mining: state-of-the-art and future trends. Business Process Management Journal 14(1), 5–22 (2008)
van der Aalst, W.M.P.: Business alignment: using process mining as a tool for delta analysis and conformance testing. Requirements Engineering 10(3), 198–211 (2005)
Hornix, P.: Performance analysis of business processes through process mining. Master’s thesis, Eindhoven University of Technology (2007)
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16, 1128–1142 (2004)
Weijters, A., van der Aalst, W., de Medeiros, A.A.: Process mining with the heuristics miner algorithm. BETA Working Paper Series WP 166, Eindhoven University of Technology (2006)
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)
Günther, C.W., van der Aalst, W.M.P.: 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)
van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering social networks from event logs. Computer Supported Cooperative Work 14(6), 549–593 (2005)
Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decision Support Systems 46(1), 300–317 (2008)
Veiga, G.M., Ferreira, D.R.: Understanding Spaghetti Models with Sequence Clustering for ProM. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 92–103. Springer, Heidelberg (2010)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99(12), 7821–7826 (2002)
van der Aalst, W.M.P., Song, M.: Mining Social Networks: Uncovering Interaction Patterns in Business Processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Heidelberg (2004)
Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)
Murtagh, F.: A survey of recent advances in hierarchical clustering algorithms. Computer Journal 26(4), 354–359 (1983)
Lv, T.-Y., Su, T.-X., Wang, Z.-X., Zuo, W.-L.: An Auto-Stopped Hierarchical Clustering Algorithm Integrating Outlier Detection Algorithm. In: Fan, W., Wu, Z., Yang, J. (eds.) WAIM 2005. LNCS, vol. 3739, pp. 464–474. Springer, Heidelberg (2005)
Han, K.J., Narayanan, S.S.: A robust stopping criterion for agglomerative hierarchical clustering in a speaker diarization system. In: Proceedings of InterSpeech, Antwerp, Belgium, pp. 1853–1856 (2007)
Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50(2), 159–179 (1985)
Jung, Y., Park, H., Du, D.Z., Drake, B.L.: A decision criterion for the optimal number of clusters in hierarchical clustering. Journal of Global Optimization 25(1), 91–111 (2003)
Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E 70(6), 066111 (2004)
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: 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)
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Ferreira, D.R., Alves, C. (2012). Discovering User Communities in Large Event Logs. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds) Business Process Management Workshops. BPM 2011. Lecture Notes in Business Information Processing, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28108-2_11
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DOI: https://doi.org/10.1007/978-3-642-28108-2_11
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