Process Variant Analysis Across Continuous Features: A Novel Framework

TL;DR

This paper introduces a new framework for analyzing process variants across continuous features using a sliding window and earth mover's distance, enabling better process segmentation and comparison.

Contribution

It presents a novel approach combining sliding window and earth mover's distance for segmenting and analyzing process variants based on continuous features.

Findings

Effective segmentation of process cases based on continuous features.

Identification of abnormal process behaviors.

Practical applicability demonstrated through a real-life case study.

Abstract

Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only identifies the variability over time, we focus on other dimensions that may play a role in the performance of the process. This research addresses the challenge of effectively segmenting cases within operational processes based on continuous features, such as duration of cases, and evaluated risk score of cases, which are often overlooked in traditional process analysis. We present a novel approach employing a sliding window technique combined with the earth mover's distance to detect changes in control flow behavior over continuous dimensions. This approach enables case segmentation, hierarchical merging of similar segments, and pairwise comparison of them, providing a comprehensive perspective on process behavior. We validate our methodology through a real-life case study in collaboration with UWV, the Dutch employee insurance agency, demonstrating its practical applicability. This research contributes to the field by aiding organizations in improving process efficiency, pinpointing abnormal behaviors, and providing valuable inputs for process comparison, and outcome prediction.