Timestamp Repair for Business Process Event Logs

Abstract

This paper contributes an approach for automatically correcting timestamp errors in business process execution logs. These errors are quite common in practice due to the logging granularity or the performance load of the logging system. Analyzing logs that have not been properly screened for such problems is likely to lead to wrong or misleading process insights. The proposed approach revolves around two techniques: one to reorder events with erroneous timestamps, the other to assign an estimated timestamp to each such event. The approach has been implemented in a software tool and extensively evaluated in different settings, using both synthetic and real-life logs. The experiments show that the approach significantly reduces the amount of incorrect timestamps, while the reordering of events scales well to large and complex datasets. The evaluation is complemented by a case study in the meat & livestock domain showing the usefulness of the approach in practice.