Process mining deals with the extraction of knowledge from event logs. One important task within this research field is denoted as conformance checking, which aims to diagnose deviations and discrepancies between modeled behavior and real-life, observed behavior. Conformance checking techniques still face some challenges, among which scalability, timeliness and traceability issues. In this paper, we propose a novel conformance analysis methodology to support the real-time monitoring of event-based data streams, which is shown to be more efficient than related approaches and able to localize deviations in a more fine-grained manner. Our developed approach can be directly applied in business process contexts where rapid reaction times are crucial; an exhaustive case example is provided to evidence the validity of the approach.
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