Sublinear Computation Paradigm: Algorithmic Revolution in the Big Data Era

The "sublinear computation paradigm" is a new approach to big data analysis that requires linear, sublinear, and constant time algorithms to handle the unprecedented explosion of big data sets. This open access book provides an overview of cutting-edge work on the paradigm, which was proposed in a large academic research project in Japan from October 2014 to March 2020. The book consists of five parts and presents high-level academic research results of strong computational and algorithmic interest.

Format: Hardback
Length: 410 pages
Publication date: 20 October 2021
Publisher: Springer Verlag, Singapore

The "sublinear computation paradigm" is a novel approach to big data analysis that was proposed in the "Foundations of Innovative Algorithms for Big Data" project, a multiyear academic research initiative conducted in Japan from October 2014 to March 2020. With the rapid growth of big data sets in research, industry, and society, there is a pressing need to develop innovative methods and approaches for efficient data processing. This book provides an overview of cutting-edge work in this field.

In the past, polynomial-time algorithms were considered fast, but when applied to large-scale big data sets, they encounter computational resource limitations or running time issues. To address this critical bottleneck, linear, sublinear, and constant time algorithms are required. The sublinear computation paradigm is proposed as a solution to support innovation in the big data era.

The book is organized into five parts: Part I introduces the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results.

The project involved three teams that focused on developing computational procedures, data structures, and modelling techniques for big data. The work produced high-level academic research results with strong computational and algorithmic interest, which are presented in this book.

The sublinear computation paradigm offers several advantages over traditional approaches. It can efficiently handle large-scale data sets by leveraging parallelism and distributed computing techniques. Additionally, sublinear algorithms are often more scalable and require less computational resources compared to their linear counterparts.

The book provides a comprehensive overview of the sublinear computation paradigm and its applications in various fields, including machine learning, data mining, graph analytics, and optimization. It covers a wide range of topics, including algorithms, data structures, and modelling techniques, and demonstrates their effectiveness in solving real-world problems.

The authors of this book are leading experts in the field of big data analysis and have made significant contributions to the development of innovative algorithms and techniques. The book is a valuable resource for researchers, practitioners, and students interested in advancing their knowledge and expertise in big data analysis.

In conclusion, the "sublinear computation paradigm" is a promising approach to big data analysis that has the potential to revolutionize the way we handle large-scale data sets. This book provides a comprehensive overview of the paradigm and its applications, and serves as a valuable resource for researchers, practitioners, and students interested in advancing their knowledge and expertise in this field.

Weight: 793g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811640940
Edition number: 1st ed. 2022