Efficient Keyword Search on Graphs using MapReduce

被引：0

作者：

Hao, Yifan ^{[1
]}

Cao, Huiping ^{[1
]}

Qi, Yan ^{[2
]}

Hu, Chuan ^{[1
]}

Brahma, Sukumar ^{[1
]}

Han, Jingyu ^{[3
]}

机构：

[1] New Mexico State Univ, Las Cruces, NM 88003 USA

[2] Turn Inc, San Francisco, CA USA

[3] Nanjing Univ Posts & Telecommun, Nanjing, Jiangsu, Peoples R China

来源：

PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA | 2015年

基金：

美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A solution of a keyword query over graphs is a Group Steiner tree, which is rooted at a node and whose nodes collectively satisfy the query (e.g. node keywords cover all the query keywords), and in which the sum of edge weights satisfies given conditions (e.g., need to be minimum or be the first K minimal among all possible sub-graphs satisfying the query). Most existing techniques for evaluating keyword queries over graphs run on a centralized computer. We propose a new approach, SOverlapping, to evaluate keyword queries over graphs on MapReduce framework by utilizing probabilistic theory to partition graphs. The new approach has shown to be effective and efficient when tested on real graph data sets.

引用

页码：2871 / 2873

页数：3