Merging Event Logs with Many to Many Relationships.

Process mining techniques enable the discovery and analysis of business processes, identifying opportunities for improvement. However, processes are often comprised of separately managed procedures that have separate log files, impossible to mine in an integrative manner. A preprocessing step that merges log files is quite straightforward when the logs have common case IDs. However, when cases in the different logs have many-to-many relationships among them this is more challenging. In this paper we present an approach for merging event logs which is capable of dealing with all kinds of relationships between logs, one-to-one or many-to-many. The approach matches cases in the logs, using temporal relations and text mining techniques. We have implemented the algorithm and tested it on a comprehensive set of synthetic logs.