Overview
- Demonstrates the importance of the algebraic approach for data processing
- Provides theoretical justifications for heuristic techniques new more efficient data processing methods
- Shows that the algebraic approach is also helpful in analyzing and developing computational methods
Part of the book series: Studies in Big Data (SBD, volume 115)
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Table of contents (37 chapters)
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Front Matter
Keywords
About this book
The book explores a new general approach to selecting—and designing—data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms.
The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions.
That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well.
The book is recommended to researchers and practitioners who need to select a data processing technique—or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.
Authors and Affiliations
Bibliographic Information
Book Title: Algebraic Approach to Data Processing
Book Subtitle: Techniques and Applications
Authors: Julio C. Urenda, Vladik Kreinovich
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-031-16780-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-16779-9Published: 16 October 2022
Softcover ISBN: 978-3-031-16782-9Published: 17 October 2023
eBook ISBN: 978-3-031-16780-5Published: 15 October 2022
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XIII, 250
Number of Illustrations: 4 b/w illustrations, 4 illustrations in colour
Topics: Data Engineering, Computational Intelligence, Big Data