Techniques for a Posteriori Analysis of Declarative Processes

Abstract

The increasing availability of event data recorded by information systems, electronic devices, web services and sensor networks provides detailed information about the actual processes in systems and organizations. Process mining techniques can use such event data to discover processes and check the conformance of process models. For conformance checking, we need to analyze whether the observed behavior matches the modeled behavior. In such settings, it is often desirable to specify the expected behavior in terms of a declarative process model rather than of a detailed procedural model. However, declarative models do not have an explicit notion of state, thus making it more difficult to pinpoint deviations and to explain and quantify discrepancies. This paper focuses on providing high-quality and understandable diagnostics. The notion of activation plays a key role in determining the effect of individual events on a given constraint. Using this notion, we are able to show cause-and-effect relations and measure the healthiness of the process.