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Book synopsis

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.

Book details

Book category Książki po angielsku Computing & information technology Databases

• Full title: Large-scale Graph Analysis: System, Algorithm and Optimization
• Author: Yingxia Shao, Bin Cui, Lei Chen
• Language: English
• Binding: Hardback
• Number of pages: 146
• EAN: 9789811539275
• ID: 25826475
• Publisher: Springer Verlag, Singapore
• Weight: 454 g
• Dimensions: 235 × 155 × 14 mm
• Date of publishing: 02. July 2020

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