Cost-Aware and Distance-Constrained Collective Spatial Keyword Query

Abstract

With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geo-textual data include geospatial locations, textual descriptions or keywords, and various attributes (e.g., a point-of-interest has its expenses and users' ratings). One prominent type of spatial keyword queries is to find, for a query consisting of a location and keywords, a set of objects that covers all the keywords and is of good quality according to some criteria. Existing studies define the criteria either based on the geospatial information of the objects solely or simply treat the geospatial and attribute information of the objects together without differentiation. As a result, they cannot provide users flexibility to express finer grained preferences on the objects. In this paper, we propose a new criterion which is to find a set of objects where the distance (defined based on the geospatial information) is at most a threshold specified by users and the cost (defined based on the attribute information) is optimized. We develop a suite of three algorithms including an exact algorithm and two approximation algorithms with provable guarantees for the problem. We conducted extensive experiments on real datasets which verified the efficiency and effectiveness of proposed algorithms.