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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]
Copyright of WIREs: Data Mining & Knowledge Discovery is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
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  Data: Extraction, correlation, and abstraction of event data for process mining.
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  Data: <searchLink fieldCode="AR" term="%22Diba%2C+Kiarash%22">Diba, Kiarash</searchLink><br /><searchLink fieldCode="AR" term="%22Batoulis%2C+Kimon%22">Batoulis, Kimon</searchLink><br /><searchLink fieldCode="AR" term="%22Weidlich%2C+Matthias%22">Weidlich, Matthias</searchLink><br /><searchLink fieldCode="AR" term="%22Weske%2C+Mathias%22">Weske, Mathias</searchLink>
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  Data: WIREs: Data Mining & Knowledge Discovery; May/Jun2020, Vol. 10 Issue 3, p1-24, 24p
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  Data: <searchLink fieldCode="DE" term="%22PROCESS+mining%22">PROCESS mining</searchLink><br /><searchLink fieldCode="DE" term="%22ELECTRONIC+data+processing%22">ELECTRONIC data processing</searchLink><br /><searchLink fieldCode="DE" term="%22INFORMATION+storage+%26+retrieval+systems%22">INFORMATION storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+mining%22">DATA mining</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+logging%22">DATA logging</searchLink>
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  Data: 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]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of WIREs: Data Mining & Knowledge Discovery is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May/Jun2020
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