Partial Order Resolution of Event Logs for Process Conformance Checking
This addresses a practical gap in process conformance checking for business analysts and data engineers, but it is incremental as it builds on existing techniques to handle partial order uncertainty.
The paper tackles the problem of conformance checking in business processes when event logs have only partial orders due to synchronization issues or data corruption, by constructing probability distributions over possible total orders and introducing an approximation method, resulting in improved accuracy over state-of-the-art in experiments with real-world and synthetic data.
While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.