Large-scale Graph Analysis: System, Algorithm and Optimization - Bin Cui,Yingxia Shao,Lei Chen
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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 bo ... Pilns apraksts
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Aprašymas
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 fordesigning efficient large-scale graph algorithms.
Vairāk informācijas
| Autors | Bin Cui, Yingxia Shao, Lei Chen |
|---|---|
| Izdevējs | Springer Nature Singapore |
| Series | Big Data Management |
| Izlaides gads | 2021 |
| Vāka tips | Mīkstais vāks |
| EAN | 9789811539305 |