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  • Density-based reverse nearest neighbourhood search in spatial databases

    Author(s)
    Allheeib, N
    Islam, MS
    Taniar, D
    Shao, Z
    Cheema, MA
    Griffith University Author(s)
    Islam, Saiful
    Year published
    2018
    Metadata
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    Abstract
    The widespread use of location-aware services and technologies which retrieve or answer spatial queries has received much interest in today’s society. An increasing number of popular applications, such as digital maps, make use of spatial databases and associated technologies. One of the most important branches of traditional spatial queries is the reverse nearest neighbour (RNN) search. This search retrieves points of interest that consider the query facility as the nearest facility. Most of the existing works on spatial databases only focus on point of interest retrieval. There is barely any work on a region of interest ...
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    The widespread use of location-aware services and technologies which retrieve or answer spatial queries has received much interest in today’s society. An increasing number of popular applications, such as digital maps, make use of spatial databases and associated technologies. One of the most important branches of traditional spatial queries is the reverse nearest neighbour (RNN) search. This search retrieves points of interest that consider the query facility as the nearest facility. Most of the existing works on spatial databases only focus on point of interest retrieval. There is barely any work on a region of interest or neighbourhood retrieval. In this paper, we introduce the concept of a group version of reverse nearest neighbour queries called reverse nearest neighbourhood (RNNH) queries. The RNNH query finds all possible reverse nearest neighbourhoods where all the neighbourhoods consider the query facility as the nearest facility. We propose an efficient algorithm for processing snapshot RNNH queries by using R-tree index. The proposed algorithm incrementally retrieves all reverse nearest neighbourhoods of the query facility. We have conducted exhaustive experiments on both real and synthetic datasets to demonstrate the superiority of the proposed algorithm.
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    Journal Title
    Journal of Ambient Intelligence and Humanized Computing
    DOI
    https://doi.org/10.1007/s12652-018-1103-x
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Spatial data and applications
    Data management and data science not elsewhere classified
    Query processing and optimisation
    Publication URI
    http://hdl.handle.net/10072/381080
    Collection
    • Journal articles

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