Uma técnica de indexação de dados semi-estruturados para o processamento eficiente de consultas com ramificação

• Ano de defesa: 2012
• Autor(a) principal: Viana, Talles Brito
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraí­ba; BR; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Informática; Indexação; Recuperação de Informação; Dados Semi-Estruturados; Data Processing; Indexing Techniques; Information Retrieval; Semi-Structured Data; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/6076

The explosive growth of web-based information systems has created various sources and vast quantities of semi-structured data, which need to be indexed by search engines in order to allow the retrieval of documents according to user needs. However, one of the major challenges in the development of indexing techniques for semi-structured data is related to how to index not only textual but also structural content. The main issue is how to efficiently handle branching path expressions without introducing precision loss as well as undesired growth of query processing costs and index file sizes. Several proposals for indexing semistructured data can be found in the literature. Despite their relevant contributions, existing proposals suffer from at least one of the problems related to precision loss, storage space requirements and query processing costs. In such a context, this thesis proposes an efficient, lossless path-based indexing technique (named as BranchGuide) for semi-structured data, which deals with a well-defined class of branching path expressions. This well-defined class includes branching paths that allow expressing parent-child dependencies between elements in which may be imposed restrictions over the textual value of attributes of such elements. As evinced by experimental evaluation, the adoption of the BranchGuide technique results in excellent query processing time and generates smaller index file sizes than a structural join indexing technique.