The segment-page indexing method for large multidimensional queries

Abstract

This paper presents a new indexing method called segment-page indexing (SP-indexing). The design objectives of SP-indexing are 2-fold: (i) improving the range query performance of multidimensional indexing methods and (ii) providing a compromise between optimal index clustering and excessive full index reorganization overhead. Although more than 10 years of database research has resulted in a great variety of multidimensional indexing methods, most efforts have focused on data-level clustering and there has been less attempt to cluster indexes. As a result, most relevant index nodes (i.e. disk pages) are widely scattered on a disk, and thus many random disk accesses are required during query processing. SP-indexing avoids such scattering by storing the relevant nodes contiguously in a segment that contains a sequence of contiguous disk pages and improves query performance by offering sequential access within a segment. A cost model is also introduced to estimate the performance of SP-indexing taking into account the physical adjacency of pages read, as well as the number of pages accessed. The analytic performance analysis indicates that SP-indexing shows a better performance than traditional indexing methods in most cases. Experimental results demonstrate that SP-indexing improves query performance up to several times compared with traditional methods using small disk pages with respect to total elapsed time.