On the support of the similarity-aware division operator in a relational database management system

• Ano de defesa: 2019
• Autor(a) principal: Vasconcelos, Guilherme Queiroz
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Complex Data; Consultas por similaridade; Dados complexos; Divisão relacional; Relational database management system; Relational division; Similarity queries; Sistema gerenciador de banco de dados relacional; SQL
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-16102019-140149/

The Division operator (&division;) from the Relational Algebra allows simple and intuitive representation of queries with the concept of \"for all\", and thus it is required by many real applications. However, the Relational Division is unable to support the needs of modern applications that manipulate complex data, such as images, audio, long texts, genetic sequences, etc. These data are better compared for similarity, whereas the Division always compares values for equality. Recent works focused on extending the Relational Algebra and database operators to support similarity comparison. This project incorporated the Similarity-Aware Divison Operator in a Relational Database Management System (RDBMS) and studied its relationship with other query operators. We extended a similarity-oriented SQL to represent the Similarity-Aware Division Operator in a simple and intuitive manner and implemented state-of-art algorithms, internal database queries and resources for similarity data manipulation all inside the RDBMS. This solution presents strategies for efficient and improved performance queries. For semantical validation, it was performed a case study of an application that finds prospective companies able to bid in public request for tenders (RFT) using similarity comparison on RFTs documents and companies\'s catalogs. We evaluated the quality of results in a case study with real datasets from request for tenders from public brazilian food companies. In the experiments, the Similarity-Aware Division Operator was able to identify which RFT which company can participate in with 90% recall.