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Large-scale Graph Analysis: System, Algorithm and Optimization [[electronic resource] /] / by Yingxia Shao, Bin Cui, Lei Chen



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Autore: Shao Yingxia Visualizza persona
Titolo: Large-scale Graph Analysis: System, Algorithm and Optimization [[electronic resource] /] / by Yingxia Shao, Bin Cui, Lei Chen Visualizza cluster
Pubblicazione: Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Edizione: 1st ed. 2020.
Descrizione fisica: 1 online resource (XIII, 146 p. 78 illus., 30 illus. in color.)
Disciplina: 511.5
Soggetto topico: Big data
Data mining
Physics
Management information systems
Computer science
Big Data
Data Mining and Knowledge Discovery
Applications of Graph Theory and Complex Networks
Management of Computing and Information Systems
Persona (resp. second.): CuiBin
ChenLei
Nota di bibliografia: Includes bibliographical references.
Nota di contenuto: 1. Introduction -- 2. Graph Computing Systems for Large-Scale Graph Analysis -- 3. Partition-Aware Graph Computing System -- 4. Efficient Parallel Subgraph Enumeration -- 5. Efficient Parallel Graph Extraction -- 6. Efficient Parallel Cohesive Subgraph Detection -- 7. Conclusions.
Sommario/riassunto: 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.
Titolo autorizzato: Large-scale Graph Analysis: System, Algorithm and Optimization  Visualizza cluster
ISBN: 981-15-3928-6
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 996465444003316
Lo trovi qui: Univ. di Salerno
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Serie: Big Data Management, . 2522-0179