Guided Interaction Exploration in Artifact-centric Process Models
This work addresses the need for better interaction analysis in process mining for domains like finance, though it appears incremental by extending existing discovery methods to focus on interactions.
The paper tackles the problem of focusing on individual artifacts rather than interactions in artifact-centric process models by automatically discovering composite state machines from event data, providing visualization and quantification of interactions, such as highlighting strongly correlated behaviors, and has been implemented as a ProM plug-in and evaluated on real-life datasets like a Dutch financial institution's loan process.
Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data we can automatically discover composite state machines representing artifact-centric processes. Moreover, we provide ways of visualizing and quantifying interactions among different artifacts. For example, we are able to highlight strongly correlated behaviours in different artifacts. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data sets, including the personal loan and overdraft process of a Dutch financial institution.