Efficient processing of neighboring skyline queries with consideration of distance, quality, and cost

Abstract

Currently, many of the processing techniques for the location-based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their quality, cost, and neighboring relationship. We term the different types of objects with better quality and closer to each other the Neighboring skyline set (or NS set). Three new types of location-based queries, the Distance-based neighboring skyline query (Dist-NS query), the Cost-based neighboring skyline query (Cost-NS query), and the Budget-based neighboring skyline query (BGT-NS query), are presented to determine the NS sets according to user’s specific requirement. A R-tree-based index, the $$R^{a,c}$$-tree, is first designed to manage each type of objects with their locations, attributes, and costs. Then, a simultaneous traversal of the $$R^{a,c}$$-trees built on different types of objects is employed with several pruning criteria to prune the non-qualifying object sets as early as possible, so as to improve the query performance. Extensive experiments using the synthetic dataset demonstrate the efficiency and the effectiveness of the proposed algorithms.