Empowering conformance checking using Big Data through horizontal decomposition

Conformance checking unleashes the full power of process mining: techniques from this discipline enable analysis quality a model through discovery event data, identification potential deviations, and projection real traces onto models. In way, insights gained available data can be transferred to richer conceptual level, amenable for human interpretation. Unfortunately, most aforementioned functionalities are grounded in an extremely difficult fundamental problem: given observed trace model, find that closely resembles observed. This paper presents architecture supports creation distribution alignment subproblems based on innovative horizontal acyclic decomposition, disengaged conformance algorithm applied their solution. is supported by Big Data infrastructure facilitates customised gross amount data. Experiments provided testify enormous proposed, thereby opening door further research several directions.