Relational Algebra for In-Database Process Mining

TL;DR

This paper introduces a formal relational algebra operator for directly extracting process relations from operational databases, enabling more flexible and efficient in-database process mining without intermediate flat files.

Contribution

It formally defines a 'directly follows' operator in relational algebra, facilitating in-database process mining and query optimization.

Findings

Defines a 'directly follows' operator using relational algebra.

Proves equivalence properties for query optimization.

Analyzes time-complexity of the operator.

Abstract

The execution logs that are used for process mining in practice are often obtained by querying an operational database and storing the result in a flat file. Consequently, the data processing power of the database system cannot be used anymore for this information, leading to constrained flexibility in the definition of mining patterns and limited execution performance in mining large logs. Enabling process mining directly on a database - instead of via intermediate storage in a flat file - therefore provides additional flexibility and efficiency. To help facilitate this ideal of in-database process mining, this paper formally defines a database operator that extracts the 'directly follows' relation from an operational database. This operator can both be used to do in-database process mining and to flexibly evaluate process mining related queries, such as: "which employee most frequently changes the 'amount' attribute of a case from one task to the next". We define the operator using the well-known relational algebra that forms the formal underpinning of relational databases. We formally prove equivalence properties of the operator that are useful for query optimization and present time-complexity properties of the operator. By doing so this paper formally defines the necessary relational algebraic elements of a 'directly follows' operator, which are required for implementation of such an operator in a DBMS.