Abstract

Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen real-life event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.

Highlights

  • Process Mining is a scientific discipline that bridges the gap between process analytics and data analysis and focuses on the analysis of event data logged during the execution of a business process

  • The line for the least-frequent-first filtering approach is below the lines of the entropy-based filtering techniques for most of the percentages of activities removed on most event logs, which shows that entropy-based filtering enables the discovery of models with higher F-score compared to filtering out infrequent activities

  • We have shown the possible detrimental effect of the presence of chaotic activities in event logs on the quality of process models produced by process discovery techniques

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Summary

Introduction

Process Mining (van der Aalst 2016) is a scientific discipline that bridges the gap between process analytics and data analysis and focuses on the analysis of event data logged during the execution of a business process. Process discovery, which plays a prominent role in process mining, is the task of automatically generating a process model that accurately describes a business process based on such event data. The Inductive Miner (Leemans et al 2013b) process discovery algorithm provides the guarantee that it can re-discover the process model from an event log given that all pairs of activities that can directly follow each other in the process are present in the event log, i.e., the log is directly-follows complete.

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