QUERY PARADIGM TO EXTRACT MOST RELEVANT OBJECTS BY GIVEN SET OF TAGS

Nonetheless, in lots of application scenarios, users cannot precisely formulate their keywords and rather choose to choose them from some candidate keyword sets. Existing studies mainly focus regarding how to efficiently discover the top-k result set given a spatio-textual query. Spatio-textual queries retrieve probably the most similar objects regarding confirmed location along with a keyword set. Driven by these applications, we advise a manuscript query paradigm, namely reverse keyword look for spatio-textual top-k queries (RST Q). Furthermore, in information browsing applications, it's helpful to focus on the objects using the tags to which the objects have higher rankings. It returns the keywords to which a target object is a spatio-textual top-k result. Extensive experimental evaluation demonstrates the efficiency in our suggested query techniques when it comes to both computational cost and that to cost. By being able to access our prime-level nodes of KcR-tree, we are able to estimate the rankings from the target object without being able to access the particular objects. To efficiently process the brand new query, we devise a manuscript hybrid index KcR-tree to keep and summarize the spatial and textual information of objects