Discovering Redundant Activities in Event Logs for the Simplification of Process Models

TL;DR

This paper presents a method to identify and remove redundant activities from event logs to improve the clarity and quality of process models discovered through process mining techniques.

Contribution

It introduces a novel approach to detect redundant activities at the log level, enhancing process model simplicity beyond frequency-based filtering methods.

Findings

Effective detection of redundant activities in real-life logs

Improved quality of process models after log simplification

Applicable as a preprocessing step for various discovery algorithms

Abstract

Process mining acts as a valuable tool to analyse the behaviour of an organisation by offering techniques to discover, monitor and enhance real processes. The key to process mining is to discovery understandable process models. However, real-life logs can be complex with redundant activities, which share similar behaviour but have different syntax. We show that the existence of such redundant activities heavily affects the quality of discovered process models. Existing approaches filter activities by frequency, which cannot solve problems caused by redundant activities. In this paper, we propose first to discover redundant activities in the log level and, then, use the discovery results to simplify event logs. Two publicly available data sets are used to evaluate the usability of our approach in real-life processes. Our approach can be adopted as a preprocessing step before applying any discovery algorithms to produce simplify models.