Workflow instance detection: Toward a knowledge capture methodology for smart oilfields

A system that captures knowledge from experienced users is of great interest in the oil industry. An important source of knowledge is application logs that record user activities. However, most of the log files are sequential records of pre-defined low level actions. It is often inconvenient or even impossible for humans to view and obtain useful information from these log entries. Also, the heterogeneity of log data in terms of syntax and granularity makes it challenging to extract the underlying knowledge from log files. In this paper, we propose a semantically rich workflow model to capture the semantics of user activities in a hierarchical structure. The mapping from low level log entries to semantic level workflow components enables automatic aggregation of log entries and their high level representation. We model and analyze two cases from the petroleum engineering domain in detail. We also present an algorithm that detects workflow instances from log files. Experimental results show that the detection algorithm is efficient and scalable.