Keyword search over relational tables and streams

Author:

Markowetz Alexander1,Yang Yin2,Papadias Dimitris2

Affiliation:

1. University of Bonn, Germany

2. Hong Kong University of Science and Technology, Hong Kong

Abstract

Relational Keyword Search (R-KWS) provides an intuitive way to query relational data without requiring SQL, or knowledge of the underlying schema. In this article we describe a comprehensive framework for R-KWS covering snapshot queries on conventional tables and continuous queries on relational streams. Our contributions are summarized as follows: (i) We provide formal semantics, addressing the temporal validity and order of results, spanning uniformly over tables and streams; (ii) we investigate two general methodologies for query processing, graph based and operator based , that resolve several problems of previous approaches; and (iii) we develop a range of algorithms and optimizations covering both methodologies. We demonstrate the effectiveness of R-KWS, as well as the significant performance benefits of the proposed techniques, through extensive experiments with static and streaming datasets.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. NATURAL GUJARATI LANGUAGE INTERFACE TO DIRECT RETRIEVAL FROM DIVERSE INDIAN AGRICULTURE SOURCES;Towards Excellence;2022-09-30

2. ValueRank: Keyword Search of Object Summaries Considering Values;KSII Transactions on Internet and Information Systems;2019-12-31

3. Versatile Size-$l$ Object Summaries for Relational Keyword Search;IEEE Transactions on Knowledge and Data Engineering;2014-04

4. A graph-theoretic approach to optimize keyword queries in relational databases;Knowledge and Information Systems;2013-10-16

5. Scalable continual top-k keyword search in relational databases;Data & Knowledge Engineering;2013-07

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3