DistJoin: plataforma de processamento distribuído de operações de junção espacial com bases de dados dinâmicas

• Ano de defesa: 2013
• Autor(a) principal: Oliveira, Sávio Salvarino Teles de
• Orientador(a): Rodrigues, Vagner José do Sacramento
• Banca de defesa: Rodrigues, Vagner José do Sacramento, Costa, Fábio Moreira, Rocha Júnior, João Batista da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Junção espacial distribuída; Bases de dados dinâmicas; R-Tree
• Palavras-chave em Inglês: Distributed spatial join; Dynamic dataset; R-Tree
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/3313

Geographic Information Systems (GIS) have received increasing attention in research institutes and industry in recent years. A Spatial Database Managament System (SDBMS) is one of the main components of a GIS and spatial join is one of the most important operations in SDBMS. Spatial join involves the relationship between two datasets, combining the geometries according some spatial predicate, such as intersection. Due to the increasing availability of spatial data, the growing number of GIS users, and the high cost of the processing of spatial operations, distributed SGBDEs (SGBDED) have been proposed as a good option to efficiently process spatial join on a cluster. This distributed processing brings some challenges, such as the data distribution and parallel and distributed processing of spatial join. This paper presents a platform for parallel and distributed processing of spatial joins in a cluster using data distribution techniques for dynamic datasets. Studies in the literature have explored data distribution techniques for static datasets, where any update requires data redistribution. This becomes unfeasible when using large datasets with frequent updates. Therefore, this paper proposes two new data distribution techniques for dynamic datasets: Proximity Area and Grid Proximity Area. These techniques have been evaluated to determine which scenarios each technique is more appropriate for. For this purpose, these techniques are evaluated in a real environment using datasets with different characteristics. Therefore, it is possible to evaluate the spatial join operation in real scenarios with each technique.