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  • Efficient processing of reverse nearest neighborhood queries in spatial databases

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    Islam420729-Accepted.pdf (2.802Mb)
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    Author(s)
    Islam, Md Saiful
    Shen, Bojie
    Wang, Can
    Taniar, David
    Wang, Junhu
    Griffith University Author(s)
    Islam, Saiful
    Wang, Can
    Wang, John
    Year published
    2020
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    Abstract
    This paper presents a novel query for spatial databases, called reverse nearest neighborhood (RNH) query, to discover the neighborhoods that find a query facility as their nearest facility among other facilities in the dataset. Unlike a reverse nearest neighbor (RNN) query, an RNH query emphasizes on group of users instead of an individual user. More specifically, given a set of user locations U, a set of facility locations F, a query location q, a distance parameter ρ and a positive integer k, an RNH query returns all ρ-radius circles C enclosing at least k users u∈U, called neighborhoods (NH) such that the distance between ...
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    This paper presents a novel query for spatial databases, called reverse nearest neighborhood (RNH) query, to discover the neighborhoods that find a query facility as their nearest facility among other facilities in the dataset. Unlike a reverse nearest neighbor (RNN) query, an RNH query emphasizes on group of users instead of an individual user. More specifically, given a set of user locations U, a set of facility locations F, a query location q, a distance parameter ρ and a positive integer k, an RNH query returns all ρ-radius circles C enclosing at least k users u∈U, called neighborhoods (NH) such that the distance between q and C is less than the distance between C and any other facility f∈F. The RNH queries might have many practical applications including on demand facility placement and smart urban planning. We present an efficient approach for processing RNH queries on location data using R-tree based data indexing. In our approach, first we retrieve candidate RNH users by an efficient bound, prune and refine technique. Then, we incrementally discover RNHs of a query facility from these candidate RNH users. We also present the variants of RNH queries in spatial databases and propose solutions for them. We validate our approach by conducting extensive experiments with real datasets.
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    Journal Title
    Information Systems
    Volume
    92
    DOI
    https://doi.org/10.1016/j.is.2020.101530
    Copyright Statement
    © 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Data management and data science not elsewhere classified
    Spatial data and applications
    Query processing and optimisation
    Science & Technology
    Computer Science, Information Systems
    Reverse nearest neighborhood
    Publication URI
    http://hdl.handle.net/10072/400084
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    • Journal articles

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