Supporting rule-based process mining by user-guided discovery of resource-aware frequent patterns

Abstract

Agile processes depend on human resources, decisions and expert knowledge and are especially versatile and comprise rather complex coherencies. Rule-based process models are well-suited for modeling these processes. There exist a number of process mining approaches to discover rule-based process models from event logs. However, existing rule-based approaches are typically based on a given set of rule templates and predominately consider control flow aspects. By only considering a given set of templates, contemporary approaches underlie a representational bias. The usage of a fixed language frequently ends into insuffcient languages. In this paper we propose an approach to automatically suggest adequate resource-aware rule templates for a given domain by pre-processing the provided event log using frequent pattern mining techniques. These templates can then be instantiated and checked by process mining methods.