Large-scale graph analysis : system, algorithm and optimization / Yingxia Shao, Bin Cui, Lei Chen.
"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 workl...
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| Main Authors: | , , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Singapore :
Springer,
2020.
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| Series: | Big data management.
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| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | "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."--Publisher's website. |
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| Physical Description: | 1 online resource. |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9789811539275 9789811539282 9811539278 9811539286 |