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• W46102521 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." @default.
• W46102521 created "2016-06-24" @default.
• W46102521 creator A5031735220 @default.
• W46102521 creator A5056257612 @default.
• W46102521 date "2012-01-01" @default.
• W46102521 modified "2023-10-07" @default.
• W46102521 title "Discovering User Communities in Large Event Logs" @default.
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• W46102521 doi "https://doi.org/10.1007/978-3-642-28108-2_11" @default.
• W46102521 hasPublicationYear "2012" @default.
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