A Scalable Database for the Storage of Object-Centric Event Logs

TL;DR

This paper introduces a scalable database solution using MongoDB for storing object-centric event logs, enabling efficient process mining by avoiding full file parsing.

Contribution

It proposes a MongoDB-based storage architecture for OCELs, facilitating scalable and efficient analysis of object-centric event data.

Findings

Enables direct database querying of OCELs

Improves scalability over file-based implementations

Facilitates efficient process mining techniques

Abstract

Object-centric process mining provides a set of techniques for the analysis of event data where events are associated to several objects. To store Object-centric Event Logs (OCELs), the JSON-OCEL and JSON-XML formats have been recently proposed. However, the proposed implementations of the OCEL are file-based. This means that the entire file needs to be parsed in order to apply process mining techniques, such as the discovery of object-centric process models. In this paper, we propose a database storage for the OCEL format using the MongoDB document database. Since documents in MongoDB are equivalent to JSON objects, the current JSON implementation of the standard could be translated straightforwardly in a series of MongoDB collections.