Behavioral patterns discovery and analysis: issues and challenges

Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process’ behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. However, their discovery requires a high computational effort, and their analysis should be facilitated by a compact visualization and by a methodology helping users to understand correlations between patterns and their context. In this talk we therefore present an approach to efficiently discover contextual behavioral patterns. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. We conclude by identifying future research directions.

Prof. Daniela Grigori
University Paris Dauphine, France

Daniela Grigori is a full Professor of computer science at University Paris Dauphine-PSL since September 2011 and she has been the head of LAMSADE CNRS laboratory the last six years. Her current research interests include  process management, data and process analytics, data integration and graph analytics.  She has a number of papers in major international conferences and journals and has served as organizer and program committee  member in many conferences. She is the co-author of a book on process analytics.