Process Trace Identification from Unstructured Execution Logs

Many real world business processes are executed without explicit orchestration and hence do not generate structured execution logs. This is particularly true for the class of business processes which are executed in service delivery centers in emerging markets where rapid changes in processes and in the people executing the processes are common. In such environments, the process execution logs are usually natural language descriptions of actions performed and hence are noisy. Despite the lack of structured logs, it is crucial to know the trace of activities as they happen on the ground. Without such a visibility into the ground activities, regulatory compliance audit, process optimization, and best practices standardization are severely disabled. Process monitoring on top of unstructured execution logs has been a relatively unexplored research area. This paper proposes an approach for process trace identification from unstructured logs that applies state-of-the-art text mining techniques. It applies this approach on logs of a real-world business process used in a service delivery center and shows that individual process activities are correctly identified 90% of the time. Also, 65% of the activity traces were identified with zero errors and an additional 24% with a single error. This approach is generic and applicable to a wide array of business processes.