Beyond assumptions: A reference architecture to enable unsupervised process discovery from video data

Process mining has developed into one of the most important research streams in business process management. Despite its successful application to improve process performance in industry, there is still substantial potential to be realized in the coming years. One of them is the use of unstructured video data to enable the analysis of previously unobservable parts of processes. Existing approaches derive event logs from video data by extracting a predefined set of potentially relevant activities. As this set is typically determined using a process model or input from process experts, rather than the available video data, current solutions are unable to identify activities that extend beyond the presumed process behavior, limiting transparency in process analysis. Therefore, this study aims to develop a solution that enables the extraction of actual process behavior from video data, as opposed to assumed process activities. Following a design science research methodology, we developed and evaluated the Reference Architecture for Video Event Extraction (RAVEE), which enables the identification of individual process steps in an unsupervised manner. We performed several evaluation activities to ensure the completeness and applicability of the RAVEE. A prototypical instantiation of the RAVEE further demonstrates its ability to extract process-relevant events from video data on two real-world datasets. • Proposes a reference architecture facilitating the extraction of actual process behavior from video data. • Extracts process steps from video data in an unsupervised manner. • Develops and evaluates the approach using the Design Science Research paradigm. • Evaluates a prototypical instantiation on publicly available datasets. • Bridges the gap between computer vision and process mining.