Direction-Aware why-not spatial keyword Top-k queries

With the continued proliferation of location-based services, a growing number of web-Accessible data objects are geotagged and have text descriptions. An important query over such web objects is the direction-Aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-Aware why-not spatial keyword top-k query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users' directionaware queries. Experimental studies demonstrate the efficiency and effectiveness of the proposed techniques.