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Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data

  • L. Octavio Lerma
  • Vladik Kreinovich

Part of the Studies in Big Data book series (SBD, volume 29)

Table of contents

  1. Front Matter
    Pages i-viii
  2. L. Octavio Lerma, Vladik Kreinovich
    Pages 1-5
  3. L. Octavio Lerma, Vladik Kreinovich
    Pages 7-43
  4. L. Octavio Lerma, Vladik Kreinovich
    Pages 45-63
  5. L. Octavio Lerma, Vladik Kreinovich
    Pages 65-112
  6. L. Octavio Lerma, Vladik Kreinovich
    Pages 113-136
  7. L. Octavio Lerma, Vladik Kreinovich
    Pages 137-137
  8. Back Matter
    Pages 139-141

About this book

Introduction

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.

Keywords

Data Processing Knowledge Processing Knowledge Use Data Acquisition Knowledge Propagation

Authors and affiliations

  • L. Octavio Lerma
    • 1
  • Vladik Kreinovich
    • 2
  1. 1.Department of Computer Science, College of EngineeringThe University of Texas at El PasoEl PasoUSA
  2. 2.Department of Computer Science, College of EngineeringThe University of Texas at El PasoEl PasoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-61349-9
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-61348-2
  • Online ISBN 978-3-319-61349-9
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site
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