A Survey of Approaches for Event Sequence Analysis and Visualization using the ESeVis Framework
This work addresses the problem of fragmented approaches in event sequence analysis for researchers and practitioners across industries like healthcare and logistics, but it is incremental as it synthesizes existing methods rather than introducing new ones.
The paper tackles the lack of integration between information visualization and process mining techniques for event sequence analysis by developing the ESeVis framework, which maps and compares contributions from both fields to provide an integrated perspective and identify synergies for future research.
Event sequence data is increasingly available. Many business operations are supported by information systems that record transactions, events, state changes, message exchanges, and so forth. This observation is equally valid for various industries, including production, logistics, healthcare, financial services, education, to name but a few. The variety of application areas explains that techniques for event sequence data analysis have been developed rather independently in different fields of computer science. Most prominent are contributions from information visualization and from process mining. So far, the contributions from these two fields have neither been compared nor have they been mapped to an integrated framework. In this paper, we develop the Event Sequence Visualization framework (ESeVis) that gives due credit to the traditions of both fields. Our mapping study provides an integrated perspective on both fields and identifies potential for synergies for future research.