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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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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