Efficient Processing of Narrow Range Queries in Multi-dimensional Data Structures (original) (raw)

2006, 2006 10th International Database Engineering and Applications Symposium (IDEAS'06)

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing nonstructured text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing. These are, data structures to indexing metric and vector spaces. The R-tree, R*-tree, and UB-tree are representatives of the vector data structures. These data structures provide efficient processing for many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is a significant type of the range query. Its processing is inefficient in the vector data structures. Moreover, the efficiency decreases from increase dimension of an indexed space. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the R-tree but native functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the Signature R-tree. This data structure is more resistant to the curse of dimensionality.