Authors:
Mahsa Pourbafrani
;
Firas Gharbi
and
Wil M. P. van der Aalst
Affiliation:
Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany
Keyword(s):
Process Mining, Event Logs, Time Series Analysis, Process Diagnostics, Performance Analysis, Coarse-Grained Process Logs, Concept Drift.
Abstract:
Process mining enables the discovery of actionable insights from event data of organizations. Process analysis techniques typically focus on process executions at detailed, i.e., fine-grained levels, which might lead to missed insights. For instance, the relation between the waiting time of process instances and the current states of the process including resources workload is hidden at fine-grained level analysis. We propose an approach for coarse-grained diagnostics of processes while decreasing user dependency and ad hoc decisions compared to the current approaches. Our approach begins with the analysis of processes at fine-grained levels focusing on performance and compliance and proceeds with an automated translation of processes to the time series format, i.e., coarse-grained process logs. We exploit time series analysis techniques to uncover the underlying patterns and potential causes and effects in processes. The evaluation using real and synthetic event logs indicates the e
fficiency of our approach to discover overlooked insights at fine-grained levels.
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