Large-scale Graph Analysis: System, Algorithm and Optimization (Paperback, 1st ed. 2020)

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms - the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Big Data Management
Release date:	July 2021
First published:	2020
Authors:	Yingxia Shao • Bin Cui • Lei Chen
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	146
Edition:	1st ed. 2020
ISBN-13:	978-981-15-3930-5
Categories:	Books > Science & Mathematics > Physics > General Books > Computing & IT > Social & legal aspects of computing > Human-computer interaction Books > Science & Mathematics > Mathematics > Applied mathematics > General Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	981-15-3930-8
Barcode:	9789811539305

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