标题:Distributed top-k keyword search over very large databases with mapreduce
作者:Yu, Ziqiang ;Yu, Xiaohui ;Chen, Yuehui ;Ma, Kun
通讯作者:Yu, Xiaohui
作者机构:[Yu, Ziqiang ;Chen, Yuehui ;Ma, Kun ] School of Information Science and Engineering, University of Jinan, Jinan; Shandong; 250101, China;[Yu, Xiaohui 更多
会议名称:5th IEEE International Congress on Big Data, BigData Congress 2016
会议日期:27 June 2016 through 2 July 2016
来源:Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016
出版年:2016
页码:349-352
DOI:10.1109/BigDataCongress.2016.55
关键词:Data graph; Distributed; Keyword search; MapReduce
摘要:In the last decade, keyword search over relational databases has been extensively studied because it promises to allow users lacking knowledge of structured query languages or unaware of the database schema to query the database in an intuitive way. The existing works about keyword search on databases proposed many approaches and have gain remarkable results. However, most of these approaches are designed for the centralized setting where keyword search is processed by only a single server. In reality, the scale of databases increases sharply and centralized methods hardly can handle keyword queries over these large databases. Moreover, processing keyword search over relational databases is a very time-consuming task, and the efficiency of the existing centralized approaches will degrade notably because the single server cannot provide enough computation power for the keyword search over very large databases. To address these challenges, we propose a distributed keyword search (DKS) approach with MapReduce and this approach can be well deployed on a cluster of servers to deal with keyword search over large databases in a parallel way. © 2016 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994666683&doi=10.1109%2fBigDataCongress.2016.55&partnerID=40&md5=e737fedcc3cfabeda5e23db305482846
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