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

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.

List of contents

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

Summary

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

Product details

• Authors: Vladik Kreinovich, L Octavi Lerma, L Octavio Lerma, L. Octavio Lerma
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 01.01.2018
• EAN: 9783319870588
• ISBN: 978-3-31-987058-8
• No. of pages: 141
• Dimensions: 155 mm x 8 mm x 235 mm
• Weight: 242 g
• Illustrations: VIII, 141 p.
• Series: Studies in Big Data
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; B, Big Data, Data Mining, Datenbanken, Artificial Intelligence, engineering, IT in Business, Data Mining and Knowledge Discovery, Business mathematics & systems, Big Data/Analytics, Computational Intelligence, Expert systems / knowledge-based systems, Databases