  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.
