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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 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.
Produkteigenschaften
- Artikelnummer: 9789811539305
- Medium: Buch
- ISBN: 978-981-15-3930-5
- Verlag: Springer Nature Singapore
- Erscheinungstermin: 02.07.2021
- Sprache(n): Englisch
- Auflage: 1. Auflage 2020
- Serie: Big Data Management
- Produktform: Kartoniert
- Gewicht: 254 g
- Seiten: 146
- Format (B x H x T): 155 x 235 x 9 mm
- Ausgabetyp: Kein, Unbekannt