Publication

Abstract : Query processing of high dimensional data with huge volume of records, especially in non-spatial domain require efficient multidimensional index. The present versions of DBMSs follow a single dimension indexing at multiple levels or indexing based on the formation of compound keys which is concatenation of the key values of the required attributes. The underlying structures, data models and query languages are not sufficient for the retrieval of information based on more complex data in terms of dimensions and size. This paper aims at designing an efficient indexing structure for multidimensional data access in non-spatial domain. This new indexing structure is evolved from R-tree with certain preprocessing steps to be applied on non-spatial data. The proposed indexing model, NSB-Tree (Non-Spatial Block tree) is balanced and has better performance than traditional B-trees and has less complicated algorithms as compared to UB tree. It has linear space complexity and logarithmic time complexity. The main drive of NSB tree is multidimensional indexing eliminating the need for multiple secondary indexes and concatenation of multiple keys. We cannot index non-spatial data using R-tree in the available DBMSs. Our index structure replaces an arbitrary number of secondary indexes for multicolumn index structure. This is implemented and feasibility check is done using the PostgreSQL database.