Proceedings of the VLDB Endowment · 2016 · 39 citations · 25 references
ComputingComputer ScienceGeoinformaticsDatabasesGeographic Information ScienceGeographic Information SystemsGeographic Information RetrievalNearest Neighbor SearchQuery ProcessingSearch SystemsSpatial Database SystemsSpatial DatabasesGeospatial Information RetrievalK-nearest Neighbor SearchNearest Neighbor QueriesNearest Neighbor RetrievalProximityProximity SearchSpatial SearchSpatial Similarity Search
The problem of maximizing bichromatic reverse k nearest neighbor queries (BR k NN) has been extensively studied in spatial databases. In this work, we present a related query for spatial-textual databases that finds an optimal location, and a set of keywords that maximizes the size of bichromatic reverse spatial textual k nearest neighbors (MaxBRST k NN). Such a query has many practical applications including social media advertisements where a limited number of relevant advertisements are displayed to each user. The problem is to find the location and the text contents to include in an advertisement so that it will be displayed to the maximum number of users. The increasing availability of spatial-textual collections allows us to answer these queries for both spatial proximity and textual similarity. This paper is the first to consider the MaxBRST k NN query. We show that the problem is NP-hard and present both approximate and exact solutions.
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