2023/01/26 by Khan, Asjad, Arsal Huda, Huda, Arsal +4
Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Business Process Modeling and Analysis #Data Quality and Management #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2301.10927
openalex publication_date 2023/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.