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  • Q+Tree: An Efficient Quad Tree based Data Indexing for Parallelizing Dynamic and Reverse Skylines

    Author(s)
    Islam, Md Saiful
    Liu, Chengfei
    Rahayu, Wenny
    Anwar, Tarique
    Griffith University Author(s)
    Islam, Saiful
    Year published
    2016
    Metadata
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    Abstract
    Skyline queries play an important role in multi-criteria decision making applications of many areas. Given a dataset of objects, a skyline query retrieves data objects that are not dominated by any other data object in the dataset. Unlike standard skyline queries where the different aspects of data objects are compared directly, dynamic and reverse skyline queries adhere to the around-by semantics, which is realized by comparing the relative distances of the data objects w.r.t. a given query. Though, there are a number of works on parallelizing the standard skyline queries, only a few works are devoted to the parallel ...
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    Skyline queries play an important role in multi-criteria decision making applications of many areas. Given a dataset of objects, a skyline query retrieves data objects that are not dominated by any other data object in the dataset. Unlike standard skyline queries where the different aspects of data objects are compared directly, dynamic and reverse skyline queries adhere to the around-by semantics, which is realized by comparing the relative distances of the data objects w.r.t. a given query. Though, there are a number of works on parallelizing the standard skyline queries, only a few works are devoted to the parallel computation of dynamic and reverse skyline queries. This paper presents an efficient quad-tree based data indexing scheme, called Q+Tree, for parallelizing the computations of the dynamic and reverse skyline queries. We compare the performance of Q+Tree with an existing quad-tree based indexing scheme. We also present several optimization heuristics to improve the performance of both of the indexing schemes further. Experimentation with both real and synthetic datasets verifies the efficiency of the proposed indexing scheme and optimization heuristics.
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    Conference Title
    CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
    Volume
    24-28-October-2016
    DOI
    https://doi.org/10.1145/2983323.2983764
    Subject
    Data structures and algorithms
    Data management and data science not elsewhere classified
    Query processing and optimisation
    Publication URI
    http://hdl.handle.net/10072/370081
    Collection
    • Conference outputs

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