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Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	Juli 2020
Genre:	EDV / Informatik
	Cohesivesubgraphdetection / Data Mining / Distributedgraphcomputing / Graphalgorithmoptimization / graphmatching / Graphpartition-awaresystem / Kombinatorik und Graphentheorie / Large-scalegraphanalysis / Parallelgraphcomputing / Subgraphenumeration / Wirtschaftsmathematik und -informatik, IT-Management mehr...
ISBN:	9789811539275
EAN-Code:	9789811539275
Verlag:	Springer
Einband:	Gebunden
Sprache:	English
Serie:	Big Data Management
Dimensionen:	H 241 mm / B 160 mm / D 15 mm
Gewicht:	412 gr
Seiten:	160
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.