对声明式近似选择谓词进行基准测试
Benchmarking Declarative Approximate Selection Predicates
摘要：
声明性数据质量一直是一个活跃的研究课题。声明性数据质量方法背后的基本原则是使用声明性语句在任何关系数据源之上实现数据质量原语。这种方法的主要优点是易于使用和与现有应用程序集成。在过去，已经提出了几个相似性谓词，用于常见的质量原语（近似选择，连接等）。并且已经使用声明性SQL语句完全表达。本文基于概率信息检索的概念，提出了新的相似性谓词及其声明式实现。然后，充分声明规范以前提出的相似性谓词在文献中，根据其主要特征分组为类。最后，一个彻底的性能和准确性的研究比较了大量的相似性谓词的数据清洗操作。
英文摘要：
Declarative data quality has been an active research topic. The fundamental principle behind a declarative approach to data quality is the use of declarative statements to realize data quality primitives on top of any relational data source. A primary advantage of such an approach is the ease of use and integration with existing applications. Several similarity predicates have been proposed in the past for common quality primitives (approximate selections, joins, etc.) and have been fully expressed using declarative SQL statements. In this thesis, new similarity predicates are proposed along with their declarative realization, based on notions of probabilistic information retrieval. Then, full declarative specifications of previously proposed similarity predicates in the literature are presented, grouped into classes according to their primary characteristics. Finally, a thorough performance and accuracy study comparing a large number of similarity predicates for data cleaning operations is performed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
