SCALING SEMANTIC GRAPH DATABASES IN SIZE AND PERFORMANCE

被引：10

作者：

Morari, Alessandro ^{[1
]}

Castellana, Vito Giovanni ^{[2
]}

Villa, Oreste ^{[3
]}

Tumeo, Antonino ^{[2
]}

Weaver, Jesse ^{[1
]}

Haglin, David ^{[1
]}

Choudhury, Sutanay ^{[4
]}

Feo, John ^{[5
,6
]}

机构：

[1] Pacific NW Natl Lab, Data Intens Sci Comp Grp, Richland, WA 99352 USA

[2] Pacific NW Natl Lab, High Performance Comp Grp, Richland, WA 99352 USA

[3] Nvidia Res, Architecture Grp, Santa Clara, CA USA

[4] Pacific NW Natl Lab, Computat Sci & Math Div, Sci Data Management Grp, Richland, WA 99352 USA

[5] High Performance Data Analyt Project, Stanford, CA USA

[6] Pacific NW Natl Lab, Richland, WA 99352 USA

来源：

IEEE MICRO | 2014年 / 34卷 / 04期

关键词：

big data; cluster; data aggregation; data analysis; distributed systems; graph databases; multithreading; SPARQL;

D O I：

10.1109/MM.2014.39

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

GEMS IS A FULL SOFTWARE SYSTEM THAT IMPLEMENTS A LARGE-SCALE, SEMANTIC GRAPH DATABASE ON COMMODITY CLUSTERS. ITS FRAMEWORK COMPRISES A SPARQL-TO-C++ COMPILER, A LIBRARY OF DISTRIBUTED DATA STRUCTURES, AND A CUSTOM MULTITHREADED RUNTIME LIBRARY. THE AUTHORS EVALUATED THEIR SOFTWARE STACK ON THE BERLIN SPARQL BENCHMARK WITH DATASETS OF UP TO 10 BILLION GRAPH EDGES, DEMONSTRATING SCALING IN DATASET SIZE AND PERFORMANCE AS THEY ADDED CLUSTER NODES.

引用

页码：16 / 26

页数：11

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