Análise da evolução temporal de dados métricos

Abstract

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.