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Towards analytical techniques for op...

Kreinovich, Vladik.

Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big databy L. Octavio Lerma, Vladik Kreinovich.
Author:	Lerma, L. Octavio.
other author:	Kreinovich, Vladik.
Published:	Cham :Springer International Publishing :2018.
Description:	viii, 141 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Electronic data processingDistributed processing.
Online resource:	http://dx.doi.org/10.1007/978-3-319-61349-9
ISBN:	9783319613499$q(electronic bk.)

Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data
Lerma, L. Octavio.

Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data[electronic resource] /by L. Octavio Lerma, Vladik Kreinovich. - Cham :Springer International Publishing :2018. - viii, 141 p. :ill., digital ;24 cm. - Studies in big data,v.292197-6503 ;. - Studies in big data ;v.1..

Introduction -- Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing -- Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.

This book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data--we mostly rely on experts' opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.

ISBN: 9783319613499$q(electronic bk.)

Standard No.: 10.1007/978-3-319-61349-9doiSubjects--Topical Terms:

182427
Electronic data processing
--Distributed processing.

LC Class. No.: QA76.9.D5

Dewey Class. No.: 004.6

Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data
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245 1 0 $a Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data $h [electronic resource] / $c by L. Octavio Lerma, Vladik Kreinovich.
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505 0 $a Introduction -- Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing -- Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.
520 $a This book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data--we mostly rely on experts' opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.
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