Self-Healing Event Logs

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Event logs of process-aware information systems play an increasingly critical role in today's enterprises because they are the basis for a number of business intelligence applications such as complex event processing, provenance analysis, performance analysis, and process mining. However, due to incorrect manual recording, system errors, and resource constraints, event logs inevitably contain noise in the form of deviating event sequences with redundant, missing, or dislocated events. To repair event logs, existing approaches rely on predefined process models to obtain a minimum recovery for each deviating event sequence. However, process models are typically unavailable in practice, rendering existing approaches inapplicable. In this scenario, can event logs be self-healing? To address this problem, we propose an approach that leverages compliant event sequences to repair deviating sequences. Our approach is effective if the compliant event sequences contain sufficient knowledge for repair. We implement our approach in a prototype and employ the tool to conduct experiments. The experimental results demonstrate that our approach can achieve efficient repairs without the help of process models.