On the efficiency of multiple range query processing in multidimensional data structures

Multidimensional data are commonly utilized in many application areas like electronic shopping, cartography and many others. These data structures support various types of queries, e.g. point or range query. The range query retrieves all tuples of a multidimensional space matched by a query rectangle. Processing range queries in a multidimensional data structure has some performance issues, especially in the case of a higher space dimension or a lower query selectivity. As result, these data are often stored in an array or one-dimensional index like B-tree and range queries are processed with a sequence scan. Many real world queries can be transformed to a multiple range query: the query including more than one query rectangle. In this article, we aim our effort to processing of this type of the range query. First, we show an algorithm processing a sequence of range queries. Second, we introduce a special type of the multiple range query, the Cartesian range query. We show optimality of these algorithms from the IO and CPU costs point of view and we compare their performance with current methods. Although we introduce these algorithms for the R-tree, we show that these algorithms are appropriate for all multidimensional data structures with nested regions.