Process discovery is a family of techniques that helps to comprehend processes from their data footprints. Yet, as processes change over time so should their corresponding models, and failure to do so will lead to models that under- or over-approximate behaviour. We present a discovery algorithm that extracts declarative processes as Dynamic Condition Response (DCR) graphs from event streams. Streams are monitored to generate temporal representations of the process, later processed to create declarative models. We validated the technique by identifying drifts in a publicly available dataset of event streams. The used metrics extend the Jaccard similarity measure to account for process change in a declarative setting. The technique and the data used for testing are available online.
In Proceedings of 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM); October 2022.