Real world applications provide many examples of unstructured processes, where process execution is mainly driven by contingent decisions taken by the actors, with the result that the process is rarely repeated exactly in the same way. In these cases, traditional Process Discovery techniques, aimed at extracting complete process models from event logs, reveal some limits. In fact, when applied to logs of unstructured processes, Process Discovery techniques usually return complex, "spaghetti-like" models, which usually provide limited support to analysts. As a remedy, in the present work we propose Behavioral Process Mining as an alternative approach to enlighten relevant subprocesses, representing meaningful collaboration work practices. The approach is based on the application of hierarchical graph clustering to the set of instance graphs generated by a process. We also describe a technique for building instance graphs from traces. We assess advantages and limits of the approach on a set of synthetic and real world experiments.

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Conformance checking allows organizations to compare process executions recorded by the IT system against a process model representing the normative behavior. Most of the existing techniques, however, are only able to pinpoint where individual process executions deviate from the normative behavior, without considering neither possible correlations among occurred deviations nor their frequency. Moreover, the actual control-flow of the process is not taken into account in the analysis. Neglecting possible parallelisms among process activities can lead to inaccurate diagnostics; it also poses some challenges in interpreting the results, since deviations occurring in parallel behaviors are often instantiated in different sequential behaviors in different traces. In this work, we present an approach to extract anomalous frequent patterns from historical logging data. The extracted patterns can exhibit parallel behaviors and correlate recurrent deviations that have occurred in possibly different portions of the process, thus providing analysts with a valuable aid for investigating nonconforming behaviors. Our approach has been implemented as a plug-in of the ESub tool and evaluated using both synthetic and real-life logs.

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