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Applications and Developments in Semantic Process Mining

Applications and Developments in Semantic Process Mining

Copyright: © 2020 |Pages: 248
ISBN13: 9781799826682|ISBN10: 1799826686|EISBN13: 9781799826705
DOI: 10.4018/978-1-7998-2668-2
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MLA

Okoye, Kingsley. Applications and Developments in Semantic Process Mining. IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2668-2

APA

Okoye, K. (2020). Applications and Developments in Semantic Process Mining. IGI Global. https://doi.org/10.4018/978-1-7998-2668-2

Chicago

Okoye, Kingsley. Applications and Developments in Semantic Process Mining. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2668-2

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As technology becomes increasingly intelligent, various factors within the field of data science are seeing significant transformation. Process analysis is one area that is undergoing substantial development due to the implementation of semantic reasoning and web technologies. The congruence of these two systems has created various applications and developments in data processing and analysis across several professional fields.

Applications and Developments in Semantic Process Mining is an essential reference source that discusses the improvement of process mining algorithms through the implementation of semantic modeling and representation. Featuring research on topics such as domain ontologies, fuzzy modeling, and information extraction, the book takes into account the different stages of process mining and its application in real time and then expounds the classical process mining techniques to semantical preparation of the extracted models for further analysis and querying at a more abstract level. The book provides a wide-ranging idea of the application and development of semantic process mining that is expected to be beneficial and used by professionals, software and data engineers, software developers, IT experts, business owners and entrepreneurs, and process analysts.

Table of Contents

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Front Materials
Title Page
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Copyright Page
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Advances in Data Mining and Database Management (ADMDM) Book Series
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Preface
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Acknowledgment
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Introduction
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Chapters
Back Materials
Conclusion
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Index
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