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. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. 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. A complete analysis methodology leveraging all the tools presented in the paper and supplemented by interpretations guidelines is also provided. Finally, experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights.

Flexibility and adaptability have been praised and empirically validated as critical for an organization's performance. They are associated with the number of different behaviors that the organization exhibits, as well as with their level of sophistication, which in turn implies the number and the quality of the available knowledge and capabilities in place. In this work, we propose the creation of a bipartite network, involving organizations and behaviors, through an event log, to capture the prospects of both organizations and behaviors for process innovation. By using the Method of Reflections, we claim that the structure of that network can expose a set of sophistication metrics, that eventually suggest i) the potential of behaviors for better organizational performance; ii) the most reachable innovation paths; iii) an "order of significance" over the behaviors considering their improvement contribution power. We show the practical potential of our approach through a Proof of Concept based on a real dataset.