Explorando o uso de árvores B+ na indexação de dados por similaridade

Abstract

Similarity queries are beneficial for retrieving complex data (such as multimedia data: images, videos, and audios), for which order relationships are not significant. In many application domains, ordering results based on distance calculations makes the data recovery process more intuitive. The optimization of operations involving similarity calculations are usually given by indexing data on methods, called Metric Access Methods (MAM). Despite the development that occurred over the last two decades, the presence of a large number of dimensions in the data degrades the performance of existing methods. In this context, this dissertation presents a new metric access method for similarity queries, such as range queries and nearest neighbor queries, through adding reference pivots to structures called B + trees. Experimental results performed with different real datasets demonstrate the effectiveness of the presented MAM, called GroupSim+.