Online Soft Conformance Checking: Any Perspective Can Indicate Deviations
This addresses the need for flexible conformance checking in process mining for scenarios lacking prescriptive models or where control-flow is not ideal, but it is incremental as it extends existing techniques to new perspectives and online operation.
The paper tackles the problem of conformance checking in process mining when prescriptive models are unavailable or control-flow perspectives are unsuitable, by proposing an approach that uses descriptive models from any perspective and works online or offline. Results from experiments with real-world and synthetic data are reported, though no specific numbers are provided.
Within process mining, a relevant activity is conformance checking. Such activity consists of establishing the extent to which actual executions of a process conform the expected behavior of a reference model. Current techniques focus on prescriptive models of the control-flow as references. In certain scenarios, however, a prescriptive model might not be available and, additionally, the control-flow perspective might not be ideal for this purpose. This paper tackles these two problems by suggesting a conformance approach that uses a descriptive model (i.e., a pattern of the observed behavior over a certain amount of time) which is not necessarily referring to the control-flow (e.g., it can be based on the social network of handover of work). Additionally, the entire approach can work both offline and online, thus providing feedback in real time. The approach, which is implemented in ProM, has been tested and results from 3 experiments with real world as well as synthetic data are reported.