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

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Název: Extraction, correlation, and abstraction of event data for process mining.
Autoři: Diba, Kiarash, Batoulis, Kimon, Weidlich, Matthias, Weske, Mathias
Zdroj: WIREs: Data Mining & Knowledge Discovery; May/Jun2020, Vol. 10 Issue 3, p1-24, 24p
Témata: PROCESS mining, ELECTRONIC data processing, INFORMATION storage & retrieval systems, DATA mining, DATA logging
Abstrakt: 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 ClusteringFundamental Concepts of Data and Knowledge > Data ConceptsTechnologies > Data Preprocessing [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt: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 ClusteringFundamental Concepts of Data and Knowledge > Data ConceptsTechnologies > Data Preprocessing [ABSTRACT FROM AUTHOR]
ISSN:19424787
DOI:10.1002/widm.1346