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Extraction, correlation, and abstraction of event data for process mining
Author(s) -
Diba Kiarash,
Batoulis Kimon,
Weidlich Matthias,
Weske Mathias
Publication year - 2019
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1346
Subject(s) - process mining , computer science , event (particle physics) , data mining , raw data , knowledge extraction , process (computing) , cluster analysis , abstraction , data science , data pre processing , business process discovery , identification (biology) , data stream mining , business process , work in process , business process management , artificial intelligence , business process modeling , engineering , physics , philosophy , operations management , operating system , botany , epistemology , quantum mechanics , biology , programming language
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