Native Directly Follows Operator
Alifah Syamsiyah, Boudewijn F. van Dongen, Remco M. Dijkman

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…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Service-Oriented Architecture and Web Services · Semantic Web and Ontologies
