In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fixed size. The Slim-tree uses the "fat-factor" which provides a simple way to quantify the degree of overlap between the nodes in a metric tree. It is well-known that the degree of overlap directly affects the query performance of index structures. There are many suggestions to reduce overlap in multi-dimensional index structures, but the Slim-tree is the first metric structure explicitly designed to reduce the degree of overlap.

Moreover, we present new algorithms for inserting objects and splitting nodes. The new insertion algorithm leads to a tree with high storage utilization and improved query performance, whereas the new split algorithm runs considerably faster than previous ones, generally without sacrificing search performance. Results obtained from experiments with real-world datasets show that the new algorithms of the Slim-tree consistently lead to performance improvements. For range queries, we observed improvements up to a factor of 35%.

19 pages

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