Journal
PROCEEDINGS OF THE VLDB ENDOWMENT
Volume 9, Issue 6, Pages 456-467Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.14778/2904121.2904122
Keywords
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Funding
- ARC Discovery Projects Scheme [DP140101587]
- ARC DECRA Research Fellowship [DE140100275]
- NICTA scholarship
- Australian Research Council [DE140100275] Funding Source: Australian Research Council
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The problem of maximizing bichromatic reverse k nearest neighbor queries (BRkNN) 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 (MaxBRSTkNN). 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 MaxBRSTkNN query. We show that the problem is NP-hard and present both approximate and exact solutions.
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