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.
| Autorius: | Yingxia Shao, Lei Chen, Bin Cui, |
| Serija: | Big Data Management |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 160 |
| ISBN-10: | 9811539308 |
| ISBN-13: | 9789811539305 |
| Formatas: | 235 x 155 x 9 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Large-scale Graph Analysis: System, Algorithm and Optimization“
