Towards Knowledge-Centric Process Mining
This work addresses a domain-specific challenge for business process management practitioners by providing an incremental improvement to handle noisy data.
The paper tackles the problem of noisy or incomplete event logs in process analytics by leveraging knowledge graphs to mitigate noise effects and support analysts in understanding variability, enabling process analytics techniques to deliver value in such real-world settings.
Process analytic approaches play a critical role in supporting the practice of business process management and continuous process improvement by leveraging process-related data to identify performance bottlenecks, extracting insights about reducing costs and optimizing the utilization of available resources. Process analytic techniques often have to contend with real-world settings where available logs are noisy or incomplete. In this paper we present an approach that permits process analytics techniques to deliver value in the face of noisy/incomplete event logs. Our approach leverages knowledge graphs to mitigate the effects of noise in event logs while supporting process analysts in understanding variability associated with event logs.