Reverse keyword search for spatio-textual top-k queries in location-based services

This paper proposes a novel query paradigm, namely reverse keyword search for spatio-textual top-k queries (RST Q). It returns the keywords under which a target object will be a spatio-textual top-k result. To efficiently process the new query, we devise a novel hybrid index KcR-tree to store and summarize the spatial and textual information of objects. To further improve the performance, we propose three query optimization techniques, i.e., KcR∗-tree, lazy upper-bound updating, and keyword set filtering. We also extend RST Q to allow the input location to be a spatial region instead of a point. Experimental results demonstrate the efficiency of our proposed query techniques in terms of both the computational cost and I/O cost.