A system for efficient and simultaneous processing of moving K nearest neighbor and spatial keyword queries

Abstract

We study the efficient, generic processing of moving K nearest neighbor (MKNN) and top-K spatial keyword (MKSK) queries. Such generic processing is attractive during high query loads. We propose GridVoronoi--an index that enables users to find the spatial nearest neighbor (NN) from uniformly distributed datasets in almost O(1) time. GridVoronoi is based upon Voronoi diagram which has proven to be highly efficient in exploring the local neighborhood of a given Voronoi cell. However, Voronoi diagram needs a method to promptly find out which Voronoi cell contains the query point. So we add a virtual (i.e., conceptual) grid to the Voronoi diagram. For any query point, GridVoronoi first uses the grid to compute which Voronoi cell contains the query, next utilizes Voronoi diagram to quickly find the NN and KNN (i.e., K nearest neighbors) of the query. Upon GridVoronoi we introduce UniSpatial framework that is able to simultaneously process MKNN and MKSK queries. For each keyword, UniSpatial builds a GridVoronoi index that enables the fast retrieval of the spatial Web objects containing this keyword. UniSpatial employs the same method to process MKNN and MKSK queries, but for MKSK queries it needs to rank the retrieved objects by their proximity to the query location and textual relevance to the input keywords. In the demo, we will use real datasets to show the functionality and performance of UniSpatial.