Extraction, correlation, and abstraction of event data for process mining

TLDR

Process mining extracts valuable knowledge of business processes from event logs, enabling end‑to‑end analysis for re‑engineering and improvement. The article investigates how raw data can be transformed into event logs suitable for process mining. It reviews and classifies techniques for extracting, correlating, and abstracting event data from diverse sources into event logs. The article is categorized under Technologies > Structure Discovery and Clustering, Fundamental Concepts of Data and Knowledge > Data Concepts, and Technologies > Data Preprocessing.

Abstract

Abstract Process mining provides a rich set of techniques to discover valuable knowledge of business processes based on data that was recorded in different types of information systems. It enables analysis of end‐to‐end processes to facilitate process re‐engineering and process improvement. Process mining techniques rely on the availability of data in the form of event logs. In order to enable process mining in diverse environments, the recorded data need to be located and transformed to event logs. The journey from raw data to event logs suitable for process mining can be addressed by a variety of methods and techniques, which are the focus of this article. In particular, techniques proposed in the literature to support the creation of event logs from raw data are reviewed and classified. This includes techniques for identification and extraction of the required event data from diverse sources as well as their correlation and abstraction. This article is categorized under: Technologies > Structure Discovery and Clustering Fundamental Concepts of Data and Knowledge > Data Concepts Technologies > Data Preprocessing