SKQAI: A novel air index for spatial keyword query processing in road networks

Abstract

Spatial keyword query (SKQ) processing is gaining great interest with the proliferation of location-based devices and services. However, most of the existing SKQ processing methods are either focused on Euclidean space or suffer from poor scalability. This paper addresses the problem of SKQ processing in road networks under wireless broadcast environments, and devises a novel air index called SKQAI, which combines a road network weighted quad-tree, several keyword quad-trees and a distance bound array, to facilitate SKQ processing in road networks. Based on SKQAI, efficient algorithms for processing Boolean Range, Top-k and Ranked SKQs are proposed. The proposed methods can efficiently prune irrelevant regions of the road network based on both road network distance and keyword information, and thus improve query processing efficiency significantly. Finally, simulation studies on two real road networks and two geo-textual datasets are conducted to demonstrate the effectiveness and efficiency of the proposed algorithms.