Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 29)
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Table of contents (6 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data
Authors: L. Octavio Lerma, Vladik Kreinovich
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-61349-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-61348-2Published: 01 September 2017
Softcover ISBN: 978-3-319-87058-8Published: 12 May 2018
eBook ISBN: 978-3-319-61349-9Published: 19 August 2017
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: VIII, 141
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Big Data, Big Data/Analytics, Artificial Intelligence