A Meta Model for Process Mining Data

Abstract. Modern process-aware information systems store detailed information about processes as they are being executed. This kind of information can be used for very different purposes. The term process mining refers to the techniques and tools to extract knowledge (e.g., in the form of models) from this. Several key players in this area have developed sophisticated process mining tools, such as Aris PPM and the HP Business Cockpit, that are capable of using the information available to generate meaningful insights. What most of these commercial process mining tools have in common is that installation and maintenance of the systems requires enormous effort, and deep knowledge of the underlying information system. Moreover, information systems log events in different ways. Therefore, the interface between process-aware information systems and process mining tools is far from trivial. It is vital to correctly map and interpret event logs recorded by the underlying information systems. Therefore, we propose a meta model for event logs. We give the requirements for the data that should be available, both informally and formally. Furthermore, we back our meta model up with an XML format called MXML and a tooling framework that is capable of reading MXML files. Although, the approach presented in this paper is very pragmatic, it can be seen as a first step towards and ontological analysis of process mining data.