Categorical Range Queries in Large Databases

Abstract

Abstract. In this paper, we introduce the categorical (a.k.a. chromatic) range queries (CRQs) in the context of large, disk-resident data sets, motivated by the fact that CRQs are conceptually simple and emerge often in DBMSs. On the basis of spatial data structures, and R-trees in particular, we propose a multi-tree index that follows the broad concept of augmenting nodes with additional information to accelerate queries. Augmentation is examined with respect to maximal/minimal points in subtrees, the properties of which are exploited by the proposed searching algorithm to effectively prune the search space. Detailed experimental results, with both real and synthetic data, illustrate the significant performance gains (up to an order of magnitude) due to the proposed method, compared to the regular range query (followed by the filtering w.r.t. categories) and to a naive R-tree augmentation method.

Approximate colored range and point enclosure queries

“…Motivated by a number of document retrieval problems, Muthukrishnan [19] studied yet another variant where the points are stored in a 1-dimensional array. Nanopoulos et al [20] studied the problem in the context of large, disk resident data sets. They proposed a multi-tree index to solve the problem but did not provide any analytical bound for their method.…”

A Survey on Optimal Route Queries for Road Networks

“…The categorical range queries [5] in the large database are handled through the paper. Mainly it is related to the spatial data like geostationary information systems.…”

“…For this technique a multi tree indent is used and which is associated with an effective way of categorical data and spatial information. The main concept is the augmentation [5] of categorical points with some information to accelerate the queries.…”

“…Optimal route query processing finds all the possible routes and then optimizes these possible routes. For that, route queries operate on the spatial data with categorical information [5], [8]. The goal is to find the optimal route from the given queries.…”

“…In this paper, we discuss about various techniques in optimal route queries. There are two main approaches: query processing for optimal route [2], [3], [4], [7] and queries that operate on spatial data [5], [6], [8]. The paper is organized as follows: section 2 presents the problems in the optimal route queries.…”