Automated Discovery of Process Models with True Concurrency and Inclusive Choices

TL;DR

This paper introduces an enhanced process discovery method that accurately captures true concurrency and inclusive choices in business processes from event logs, improving upon existing techniques that only approximate concurrency.

Contribution

It presents a refined version of the Split Miner algorithm capable of detecting true concurrency and differentiating inclusive choices from exclusive ones in process models.

Findings

Accurately identifies true concurrency in event logs.

Differentiates between inclusive and exclusive choices.

Outperforms baseline methods on real-life logs.

Abstract

Enterprise information systems allow companies to maintain detailed records of their business process executions. These records can be extracted in the form of event logs, which capture the execution of activities across multiple instances of a business process. Event logs may be used to analyze business processes at a fine level of detail using process mining techniques. Among other things, process mining techniques allow us to discover a process model from an event log -- an operation known as automated process discovery. Despite a rich body of research in the field, existing automated process discovery techniques do not fully capture the concurrency inherent in a business process. Specifically, the bulk of these techniques treat two activities A and B as concurrent if sometimes A completes before B and other times B completes before A. Typically though, activities in a business process are executed in a true concurrency setting, meaning that two or more activity executions overlap temporally. This paper addresses this gap by presenting a refined version of an automated process discovery technique, namely Split Miner, that discovers true concurrency relations from event logs containing start and end timestamps for each activity. The proposed technique is also able to differentiate between exclusive and inclusive choices. We evaluate the proposed technique relative to existing baselines using 11 real-life logs drawn from different industries.