Process mining in the large: A tutorial

Abstract

Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. On the one hand, conventional Business Process Management (BPM) andWorkflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. Here, the challenge is to turn torrents of event data ("Big Data") into valuable insights related to process performance and compliance. Fortunately, process mining results can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks. This tutorial paper introduces basic process mining techniques that can be used for process discovery and conformance checking. Moreover, some very general decomposition results are discussed. These allow for the decomposition and distribution of process discovery and conformance checking problems, thus enabling process mining in the large. © Springer International Publishing Switzerland 2014.