QDR-Tree: An Efficient Index Scheme for Complex Spatial Keyword Query

TL;DR

This paper introduces QDR-Tree, a novel hybrid index structure that efficiently supports complex spatial keyword queries with numerical attributes, improving query processing time and space utilization.

Contribution

It proposes the first Attributes-Aware Spatial Keyword Query framework and a two-layer hybrid index combining keyword clustering and dual filtering techniques.

Findings

QDR-Tree outperforms existing methods in query efficiency.

The index reduces processing time and space consumption.

Experimental results validate the effectiveness of the proposed approach.

Abstract

With the popularity of mobile devices and the development of geo-positioning technology, location-based services (LBS) attract much attention and top-k spatial keyword queries become increasingly complex. It is common to see that clients issue a query to find a restaurant serving pizza and steak, low in price and noise level particularly. However, most of prior works focused only on the spatial keyword while ignoring these independent numerical attributes. In this paper we demonstrate, for the first time, the Attributes-Aware Spatial Keyword Query (ASKQ), and devise a two-layer hybrid index structure called Quad-cluster Dual-filtering R-Tree (QDR-Tree). In the keyword cluster layer, a Quad-Cluster Tree (QC-Tree) is built based on the hierarchical clustering algorithm using kernel k-means to classify keywords. In the spatial layer, for each leaf node of the QC-Tree, we attach a Dual-Filtering R-Tree (DR-Tree) with two filtering algorithms, namely, keyword bitmap-based and attributes skyline-based filtering. Accordingly, efficient query processing algorithms are proposed. Through theoretical analysis, we have verified the optimization both in processing time and space consumption. Finally, massive experiments with real-data demonstrate the efficiency and effectiveness of QDR-Tree.