Análise da evolução temporal de dados métricos

Detalhes bibliográficos
Ano de defesa: 2016
Autor(a) principal: Fogaça, Isis Caroline Oliveira de Sousa
Orientador(a): Bueno, Renato lattes
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de São Carlos
Câmpus São Carlos
Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/8661
Resumo: The expansion of different areas of knowledge through many types of information brought the necessity to support complex data (images, sounds, videos, strings, DNA chains, etc.), that do not have a Total Order Relationship and need other management mechanisms, like the contentbased retrieval. In general, they are represented in metric space domains, where we have only the elements and the distances between them. Through the characteristics extracted from them, we perform the similarity search. Considering the necessity to associate temporal information on these data in many applications, this work aims to analyze the temporal evolve of metric data. One alternative for this is embedding them into a multidimensional space to allow trajectories estimates. We studied different methods of embedding and analyzed how this affected the data’s distribution and, consequently, the estimates. Two new methods were purposed to estimate an element’s status on a different time from that available in database, in order to reduce the number of non-relevant elements on search results. These methods are based on radius search reduction (range) and evaluation of retrieved element’s proximity by using an approximation of reverse k- NN. We performed experiments which showed that purposed methods could improve the estimate’s result, that used to be performed only using k-NN searches.