Detecting Context-Aware Deviations in Process Executions
This work addresses the need for context-aware deviation detection in business processes, such as healthcare and manufacturing, but it appears incremental as it builds on existing techniques without introducing a fundamentally new method.
The paper tackles the problem of detecting deviations in process executions by incorporating various contextual situations, and it presents a framework that was evaluated across 255 different contextual scenarios.
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.