Temporally relevant parallel top-k spatial keyword search

New spatio-textual indexing methods are needed to support efficient search and update of the massive amounts spatially referenced text being generated. Location based services using geo-tagged documents provide valuable ranked recommendations about nearby restaurants, services, sales, emergency events, visitor attractions. Consequently, top-k spatial keyword queries (TkSKQ) have received a lot attention from research community. Several indexes been proposed efficiently TkSKQ. Some these updates on live document streams, but ranking schemes employed by them do not simultaneously incorporate temporal relevance, textual similarity proximity. Moreover, existing approaches limited or no capability exploit parallelism with ingestion query execution. We present parallel index, Pastri, address aforementioned issues. Pastri can be updated incrementally over real-time streams. To temporally relevant continuously generated we propose dynamic scheme. Our approach retrieves that most at time implemented integrate it within system persistent store several thread pools various levels. Experimental evaluation involving real-world datasets synthetic (that created) demonstrates our is able sustain high throughput. Furthermore, Pastri's TkSKQ performance one two orders magnitude faster than other indexes.