A Monitoring and Discovery Approach for Declarative Processes Based on Streams

TL;DR

This paper introduces a real-time monitoring and discovery method for declarative processes using event streams, enabling dynamic updates to process models to reflect ongoing changes accurately.

Contribution

It presents a novel algorithm that extracts declarative process models as DCR graphs from streaming data, addressing the challenge of evolving processes.

Findings

Effective detection of process changes in streams

Quantitative validation using extended Jaccard similarity

Qualitative analysis linking detected changes to real process modifications

Abstract

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 behavior. 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 generate declarative models. We validated the technique via quantitative and qualitative evaluations. For the quantitative evaluation, we adopted an extended Jaccard similarity measure to account for process change in a declarative setting. For the qualitative evaluation, we showcase how changes identified by the technique correspond to real changes in an existing process. The technique and the data used for testing are available online.