Native Directly Follows Operator

TL;DR

This paper introduces a native SQL operator for direct process discovery on relational databases, improving efficiency over existing methods by reducing overhead and increasing speed in process mining tasks.

Contribution

It proposes a native SQL operator for directly computing the 'directly follows' relation, enhancing performance in process mining on large relational databases.

Findings

Faster performance than existing database approaches

Effective on large event datasets

Reduces overhead compared to traditional methods

Abstract

Typical legacy information systems store data in relational databases. Process mining is a research discipline that analyzes this data to obtain insights into processes. Many different process mining techniques can be applied to data. In current techniques, an XES event log serves as a basis for analysis. However, because of the static characteristic of an XES event log, we need to create one XES file for each process mining question, which leads to overhead and inflexibility. As an alternative, people attempt to perform process mining directly on the data source using so-called intermediate structures. In previous work, we investigated methods to build intermediate structures on source data by executing a basic SQL query on the database. However, the nested form in the SQL query can cause performance issues on the database side. Therefore, in this paper, we propose a native SQL operator for direct process discovery on relational databases. We define a native operator for the simplest form of the intermediate structure, called the "directly follows relation". This approach has been evaluated with big event data and the experimental results show that it performs faster than the state-of-the-art of database approaches.