Efficient top-k processing in large-scaled distributed environments

被引:25
作者
Zhao, Keping [2 ]
Tao, Yufei [1 ]
Zhou, Shuigeng [3 ]
机构
[1] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Sha Tin, Hong Kong, Peoples R China
[2] Microsoft China, Shanghai 200030, Peoples R China
[3] Fudan Univ, Dept Comp Sci & Engn, Shanghai 200433, Peoples R China
基金
中国国家自然科学基金;
关键词
top-k; distributed database; caching;
D O I
10.1016/j.datak.2007.03.012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The rapid development of networking technologies has made it possible to construct a distributed database that involves a huge number of sites. Query processing in such a large-scaled system poses serious challenges beyond the scope of traditional distributed algorithms. In this paper, we propose a new algorithm BRANCA for performing top-k retrieval in these environments. Integrating two orthogonal methodologies "semantic caching" and "routing indexes", BRANCA is able to solve a query by accessing only a small number of servers. Our algorithmic findings are accompanied with a solid theoretical analysis, which rigorously proves the effectiveness of BRANCA. Extensive experiments verify that our technique outperforms the existing methods significantly. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:315 / 335
页数:21
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