Dynamically ranked top-k spatial keyword search

With the growing data volume and popularity of Web services and Location-Based Services (LBS) new spatio-textual application are emerging. These applications are contributing to a deluge of geo-tagged documents. As a result, top-k spatial keyword searches have attracted a lot of attention and a number of spatio-textual indexes have been proposed. However, these indexes do not consider the "recency" of the indexed documents. Part of the challenge is due to the fact that the textual relevance score measures that these indexes use, require all documents to be inspected. To address these issues, we propose the idea of "dynamic ranking" of spatio-textual objects. We also introduce a novel index, called STARI, which uses this ranking method to retrieve the most recent top-k relevant objects. Experimental evaluation demonstrates that that our system can support high document update rates and low query latency.