A novel approach for process mining based on event types

Abstract

Despite the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), historic information is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. Given its potential and challenges it is no surprise that recently process mining has become a vivid research area. In this paper, a novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing algorithms such as the ?-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining.