Detecting Context-Aware Deviations in Process Executions

TL;DR

This paper introduces a flexible framework for context-aware deviation detection in process executions, addressing limitations of existing methods by systematically incorporating diverse contexts and distinguishing positive from negative influences.

Contribution

The work presents a novel, extendable framework and web service for context-aware deviation detection, improving upon prior approaches by handling multiple contexts and deviation types.

Findings

Effective in 255 different contextual scenarios

Outperforms existing deviation detection methods in context-awareness

Framework is extensible to various domains and techniques

Abstract

A deviation detection aims to detect deviating process instances, e.g., patients in the healthcare process and products in the manufacturing process. A business process of an organization is executed in various contextual situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of semiconductor chip shortage in the case of automobile companies. Thus, context-aware deviation detection is essential to provide relevant insights. However, existing work 1) does not provide a systematic way of incorporating various contexts, 2) is tailored to a specific approach without using an extensive pool of existing deviation detection techniques, and 3) does not distinguish positive and negative contexts that justify and refute deviation, respectively. In this work, we provide a framework to bridge the aforementioned gaps. We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods. We have evaluated the effectiveness of the proposed framework by conducting experiments using 255 different contextual scenarios.