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Relational calculus for actionable k...

Bares, Michel.

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Relational calculus for actionable knowledge

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Relational calculus for actionable knowledge/ by Michel Bares, Eloi Bosse.
作者:	Bares, Michel.
其他作者:	Bosse, Eloi.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xx, 340 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter1. Introduction to Actionable Knowledge: Concepts & Definitions -- Chapter2. Knowledge and its Dimensions -- Chapter3. The Knowledge Chain -- Chapter4. Preliminaries on Crisp and Fuzzy Relational Calculus -- Chapter5. Actionable Knowledge for Efficient Actions -- Chapter6. Relational Calculus for the Generation of Actionable Knowledge -- Chapter7. Conclusion.
Contained By:	Springer Nature eBook
標題:	Relational calculus. -
電子資源:	https://doi.org/10.1007/978-3-030-92430-0
ISBN:	9783030924300

Relational calculus for actionable knowledge
Bares, Michel.

Relational calculus for actionable knowledge[electronic resource] /by Michel Bares, Eloi Bosse. - Cham :Springer International Publishing :2022. - xx, 340 p. :ill. (some col.), digital ;24 cm. - Information fusion and data science,2510-1536. - Information fusion and data science..

Chapter1. Introduction to Actionable Knowledge: Concepts & Definitions -- Chapter2. Knowledge and its Dimensions -- Chapter3. The Knowledge Chain -- Chapter4. Preliminaries on Crisp and Fuzzy Relational Calculus -- Chapter5. Actionable Knowledge for Efficient Actions -- Chapter6. Relational Calculus for the Generation of Actionable Knowledge -- Chapter7. Conclusion.

This book focuses on one of the major challenges of the newly created scientific domain known as data science: turning data into actionable knowledge in order to exploit increasing data volumes and deal with their inherent complexity. Actionable knowledge has been qualitatively and intensively studied in management, business, and the social sciences but in computer science and engineering, its connection has only recently been established to data mining and its evolution, 'Knowledge Discovery and Data Mining' (KDD) Data mining seeks to extract interesting patterns from data, but, until now, the patterns discovered from data have not always been 'actionable' for decision-makers in Socio-Technical Organizations (STO) With the evolution of the Internet and connectivity, STOs have evolved into Cyber-Physical and Social Systems (CPSS) that are known to describe our world today. In such complex and dynamic environments, the conventional KDD process is insufficient, and additional processes are required to transform complex data into actionable knowledge. Readers are presented with advanced knowledge concepts and the analytics and information fusion (AIF) processes aimed at delivering actionable knowledge. The authors provide an understanding of the concept of 'relation' and its exploitation, relational calculus, as well as the formalization of specific dimensions of knowledge that achieve a semantic growth along the AIF processes. This book serves as an important technical presentation of relational calculus and its application to processing chains in order to generate actionable knowledge. It is ideal for graduate students, researchers, or industry professionals interested in decision science and knowledge engineering.

ISBN: 9783030924300

Standard No.: 10.1007/978-3-030-92430-0doiSubjects--Topical Terms:

3592046
Relational calculus.

LC Class. No.: QA76.9.D343 / B37 2022

Dewey Class. No.: 006.3120151

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505 0 $a Chapter1. Introduction to Actionable Knowledge: Concepts & Definitions -- Chapter2. Knowledge and its Dimensions -- Chapter3. The Knowledge Chain -- Chapter4. Preliminaries on Crisp and Fuzzy Relational Calculus -- Chapter5. Actionable Knowledge for Efficient Actions -- Chapter6. Relational Calculus for the Generation of Actionable Knowledge -- Chapter7. Conclusion.
520 $a This book focuses on one of the major challenges of the newly created scientific domain known as data science: turning data into actionable knowledge in order to exploit increasing data volumes and deal with their inherent complexity. Actionable knowledge has been qualitatively and intensively studied in management, business, and the social sciences but in computer science and engineering, its connection has only recently been established to data mining and its evolution, 'Knowledge Discovery and Data Mining' (KDD) Data mining seeks to extract interesting patterns from data, but, until now, the patterns discovered from data have not always been 'actionable' for decision-makers in Socio-Technical Organizations (STO) With the evolution of the Internet and connectivity, STOs have evolved into Cyber-Physical and Social Systems (CPSS) that are known to describe our world today. In such complex and dynamic environments, the conventional KDD process is insufficient, and additional processes are required to transform complex data into actionable knowledge. Readers are presented with advanced knowledge concepts and the analytics and information fusion (AIF) processes aimed at delivering actionable knowledge. The authors provide an understanding of the concept of 'relation' and its exploitation, relational calculus, as well as the formalization of specific dimensions of knowledge that achieve a semantic growth along the AIF processes. This book serves as an important technical presentation of relational calculus and its application to processing chains in order to generate actionable knowledge. It is ideal for graduate students, researchers, or industry professionals interested in decision science and knowledge engineering.
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