Revealing Inherent Concurrency in Event Data: A Partial Order Approach to Process Discovery
For process mining practitioners, this provides a more faithful representation of concurrent process behavior compared to traditional linearization methods.
The paper introduces a scalable algorithm that uses partial orders to discover process models from event data, preserving inherent concurrency and producing sound, perfectly fitting models. It demonstrates applicability on complex real-life event logs.
Process discovery algorithms traditionally linearize events, failing to capture the inherent concurrency of real-world processes. While some techniques can handle partially ordered data, they often struggle with scalability on large event logs. We introduce a novel, scalable algorithm that directly leverages partial orders in process discovery. Our approach derives partially ordered traces from event data and aggregates them into a sound-by-construction, perfectly fitting process model. Our hierarchical algorithm preserves inherent concurrency while systematically abstracting exclusive choices and loop patterns, enhancing model compactness and precision. We have implemented our technique and demonstrated its applicability on complex real-life event logs. Our work contributes a scalable solution for a more faithful representation of process behavior, especially when concurrency is prevalent in event data.