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Exploring Intersection Trees for Indexing Metric Spaces

Searching in a dataset for objects that are similar to a given query object is a fundamental problem for several applications that use complex data. The general problem of many similarity measures for complex objects is their computational complexity, which makes them unusable for large databases. Here, we introduce a study of a variant of a metric tree data structure for indexing and querying such data. Both a sequential and a parallel versions are introduced. The efficiency of our proposal is demonstrated through experiments on real-world data, as well as a comparison with existing techniques: MM-tree and Slim-tree.