Multi-Dimensional Event Data in Graph Databases

TL;DR

This paper introduces a graph-based data model for multi-dimensional event data that supports complex queries across multiple entities, enabling advanced process mining with existing graph query languages.

Contribution

It proposes a systematic graph-based data model for multi-entity event data, including semantics and efficient querying methods, facilitating process mining and analysis.

Findings

The data model supports complex multi-entity event queries.

Efficient conversion of real-life event data into the model.

Enables process mining using existing graph query languages.

Abstract

Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as "directly/eventually-follows", it does not support querying multi-dimensional event data of multiple related entities. Relational databases allow storing multi-dimensional event data but existing query languages do not support querying for sequences or paths of events in terms of temporal relations. In this paper, we propose a general data model for multi-dimensional event data based on labeled property graphs that allows storing structural and temporal relations in a single, integrated graph-based data structure in a systematic way. We provide semantics for all concepts of our data model, and generic queries for modeling event data over multiple entities that interact synchronously and asynchronously . The queries allow for efficiently converting large real-life event data sets into our data model and we provide 5 converted data sets for further research. We show that typical and advanced queries for retrieving and aggregating such multidimensional event data can be formulated and executed efficiently in the existing query language Cypher, giving rise to several new research questions. Specifically aggregation queries on our data model enable process mining over multiple interrelated entities using off-the-shelf technology.