Keyword search on structured and semi-structured data

Yi Chen, Wei Wang, Ziyang Liu, Xuemin Lin
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引用次数: 207

Abstract

Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.
结构化和半结构化数据的关键字搜索
授权用户使用简单的关键字访问数据库可以使用户从掌握结构化查询语言和理解复杂且可能快速发展的数据模式的陡峭学习曲线中解脱出来。在本教程中,我们概述了用于支持结构化和半结构化数据上的关键字搜索的最新技术,包括查询结果定义、排序函数、结果生成和top-k查询处理、代码片段生成、结果聚类、查询清理、性能优化和搜索质量评估。将讨论各种数据模型,包括关系数据、XML数据、图结构数据、数据流和工作流。我们还讨论了基于关键字搜索构建的应用程序,例如基于关键字的数据库选择、查询生成和分析处理。最后,我们指出了未来研究的挑战和机遇,以推动该领域的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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