Constructing Probable Explanations of Nonconformity: A Data-Aware and History-Based Approach

Auditing the execution of business processes is becoming a critical issue for organizations. Conformance checking has been proposed as a viable approach to analyze process executions with respect to a process model. In particular, alignments provide a robust approach to conformance checking in that they are able to pinpoint the causes of nonconformity. Alignment-based techniques usually rely on a predefined cost function which assigns a cost to every possible deviation. Defining such a cost function, however, is not trivial and is prone to imperfection that can result in inaccurate diagnostic information. This paper proposes an alignment-based approach to construct probable explanations of nonconformity. In particular, we show how cost functions can be automatically computed based on historical logging data and taking into account multiple process perspectives. We implemented our approach as a plug-in of the ProM framework. Experimental results show that our approach provides more accurate diagnostics compared to existing alignment-based techniques.