Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data (Studies in Big Data, Band 29) - Softcover

Buch 25 von 95: Studies in Big Data

Lerma, L. Octavio; Kreinovich, Vladik

 
9783319870588: Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data (Studies in Big Data, Band 29)

Inhaltsangabe

Develops analytical models for knowledge-related processes, from knowledge acquisition to knowledge processing and knowledge propagation

Provides various case studies explaining how the corresponding models can be used

Allows easier optimization and application by not depending on detailed numerical simulation


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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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Weitere beliebte Ausgaben desselben Titels

9783319613482: Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data (Studies in Big Data, 29, Band 29)

Vorgestellte Ausgabe

ISBN 10:  3319613480 ISBN 13:  9783319613482
Verlag: Springer, 2017
Hardcover