A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining
This work addresses a specific bottleneck in process mining for business analysts by improving event log quality, though it is incremental as it builds on existing preprocessing techniques.
The paper tackles the problem of inconsistent activity labels in event logs for process mining, which cause redundancy and complexity, by proposing a multi-view approach that automatically detects redundant labels using context-aware and semantic features, achieving efficient detection even for low-frequency labels.
Process mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process mining heavily rely on the quality of the event logs. Activities extracted from different data sources or the free-text nature within the same system may lead to inconsistent labels. Such inconsistency would then lead to redundancy in activity labels, which refer to labels that have different syntax but share the same behaviours. Redundant activity labels could introduce unnecessary complexities to the event logs. The identifications of these labels from data-driven process discovery are difficult and rely heavily on human intervention. Neither existing process discovery algorithms nor event data preprocessing techniques can solve such redundancy efficiently. In this paper, we propose a multi-view approach to automatically detect redundant activity labels using not only context-aware features such as control--flow relations and attribute values but also semantic features from the event logs. Our evaluation of several publicly available datasets and a real-life case study demonstrate that our approach can efficiently detect redundant activity labels even with low-occurrence frequencies. The proposed approach can add value to the preprocessing step to generate more representative event logs.