The discipline concerned with automatic process analysis techniques based on event data of complex systems is called process mining. Classical process mining has by and large assumed that event data is processed in a single, central data file on a device with sufficient computing power.

In plenty of use cases event data originates from distributed, sensor-based systems that do not satisfy the assumptions of (classical) process mining in the general case. Here, events can be any kind of observations (e.g., a sensor value changed), no matter if explicitly linked to a specific activities or cases. As of today, the application of process mining to distributed scenarios suffers from technical and conceptual research challenges spanning the three dimensions (1) Infrastructure-awareness: The distribution and physical properties of sensor-based systems imposes specific research challenges for efficient event data processing. (2) Data-awareness: The granularity and quality characteristics of sensor data imposes specific research challenges for meaningful and privacy-sensitive event data abstraction. (3) User-awareness: The detail and complexity of sensor data imposes specific research challenges for traceable presentation and representation of distributed process mining results.

This meeting brings together expertise from the fields of process management, data and software engineering, distributed systems, and privacy mechanisms to discuss the methodological foundations for novel process mining techniques for distributed event data.

Creative Commons BY 4.0

Wilhelm Hasselbring, Agnes Koschmider, Olaf Landsiedel, Jan Mendling, Florian Tschorsch, and Matthias Weidlich