Top-k approximate selection for typicality query results over spatio-textual data

Spatial keyword query is a classical query processing mode for spatio-textual data, which aims to provide users the spatio-textual objects with the highest spatial proximity and textual similarity to the given query. However, the top-k result objects obtained by using the spatial keyword query mode are often similar to each other, while users hope that the system can pick top-k typicality results from the candidate query results in order to make users understand the representative features of the candidate result set. To deal with the problem of typicality analysis and typical object selection of spatio-textual data query results, a typicality evaluation and top-k approximate selection approach is proposed. First, the approach calculates the synthetic distances on dimensions of geographic location, textual semantics, and numeric attributes between all spatio-textual objects. And then, a hybrid index structure that can simultaneously support the location, text, and numeric multi-dimension matching is presented in order to expeditiously obtain the candidate query results. According to the synthetic distances between spatio-textual objects, a Gaussian kernel probability density estimation-based method for measuring the typicality of query results is proposed. To facilitate the query result analysis and top-k typical object selection, the Tournament strategy-based and local neighborhood-based top-k typical object approximate selection algorithms are presented, respectively. The experimental results demonstrated that the text semantic relevancy measuring method for spatio-textual objects is accurate and reasonable, and the local neighborhood-based top-k typicality result approximate selection algorithm achieved both the low error rate and high execution efficiency. The source code and datasets used in this paper are available to be accessed from https://github.com/JiaShengS/Typicality_analysis/.