ESub: Mining and exploring substructures in knowledge-intensive processes

Abstract

Process Mining (PM) encompasses a number of methodologies designed for extracting knowledge from event logs, typically recorded by operational information systems like ERPs, Workflow Management Systems or other process-aware enterprise systems. The structured nature of processes implemented in these systems has led to the development of effective techniques for conformance checking (check if a real execution trace conforms to a predefined process schema) or process discovery (synthesize a process schema from a set of real execution traces recorded in the trace log) [1]. However in many knowledge-intensive domains, like e.g. health care, emergency management, research and innovation development, processes are typically characterized by little or no structure, since the flow of activities strongly depends on context-dependent decisions that should rely on human knowledge. Consequently, classical process discovery techniques usually provide limited support in analyzing these processes. As a further issue, in these domains an integrated information system may not even exist, requiring to integrate a number of independent event logs.