Business process management organizes work into several interrelated "units of work", fundamentally conceptualized as a task. The classical concept of a task as a single step executed by a single actor in a single case fails to capture more complex aspects of work that occur in real-life processes. For instance, actors working together or the processing of work in batches, where multiple actors and/or cases meet for a number of steps. Established process mining and modeling techniques lack concepts for dealing with these more complex manifestations of work. We leverage event graphs as a data structure to model behavior along the actor and the case perspective in an integrated model, revealing a variety of fundamentally different types of task executions. We contribute a novel taxonomy and interpretation of these task execution patterns as well as techniques for detecting these in event graphs, complementing recent research in identifying patterns of work and their changes in routine dynamics. Our evaluation on two real-life event logs shows that these non-classical task execution patterns not only exist, but make up for the larger share of events in a process and reveal changes in how actors do their work.

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Aggregation of event data is a key operation in process mining for revealing behavioral features of processes for analysis. It has primarily been studied over sequences of events in event logs. The data model of event knowledge graphs enables new analysis questions requiring new forms of aggregation. We focus on analyzing task executions in event knowledge graphs. We show that existing aggregation operations are inadequate and propose new aggregation operations, formulated as query operators over labeled property graphs. We show on the BPIC’17 dataset that the new aggregation operations allow gaining new insights into differences in task executions, actor behavior, and work division.