Shedding Light on Blind Spots: Developing a Reference Architecture to Leverage Video Data for Process Mining
This addresses the gap in process mining for business process management by enabling analysis of manual activities, though it is incremental as it builds on existing computer vision and process mining methods.
The paper tackles the problem of blind spots in process mining caused by manual activities not captured by digital systems by proposing a reference architecture to extract event logs from unstructured video data, with results showing that a prototype can automatically extract most process-relevant events.
Process mining is one of the most active research streams in business process management. In recent years, numerous methods have been proposed for analyzing structured process data. Yet, in many cases, it is only the digitized parts of processes that are directly captured from process-aware information systems, and manual activities often result in blind spots. While the use of video cameras to observe these activities could help to fill this gap, a standardized approach to extracting event logs from unstructured video data remains lacking. Here, we propose a reference architecture to bridge the gap between computer vision and process mining. Various evaluation activities (i.e., competing artifact analysis, prototyping, and real-world application) ensured that the proposed reference architecture allows flexible, use-case-driven, and context-specific instantiations. Our results also show that an exemplary software prototype instantiation of the proposed reference architecture is capable of automatically extracting most of the process-relevant events from unstructured video data.