StarStar Models: Process Analysis on top of Databases

TL;DR

This paper introduces StarStar Models, a novel approach for process analysis on top of databases that visualizes activity relationships and supports process mining without complex queries.

Contribution

It presents a new modeling technique that overlays process relationships on databases, enabling easier extraction of event logs and application of process mining techniques.

Findings

Effective visualization of activity relationships as multigraphs

Supports retrieval of classic event logs from models

Empirical evaluation demonstrates practical utility

Abstract

Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, there is a lack of techniques to display relationships on top of databases without the need to express a complex query to get the required information. In this paper, a novel modeling technique that works on top of databases is presented. This technique is able to show a multigraph representing activities inferred from database events, connected with edges that are annotated with frequency and performance information. The representation may be the entry point to apply advanced process mining techniques that work on classic event logs, as the model provides a simple way to retrieve a classic event log from a specified piece of model. Comparison with similar techniques and an empirical evaluation are provided.