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  • Computing influence of a product through uncertain reverse skyline

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    IslamPUB2262.pdf (1.468Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Islam, Md Saiful
    Rahayu, Wenny
    Liu, Chengfei
    Anwar, Tarique
    Stantic, Bela
    Griffith University Author(s)
    Stantic, Bela
    Islam, Saiful
    Year published
    2017
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    Abstract
    Understanding the influence of a product is crucially important for making informed business decisions. This paper introduces a new type of skyline queries, called uncertain reverse skyline, for measuring the influence of a probabilistic product in uncertain data settings. More specifically, given a dataset of probabilistic products P and a set of customers C, an uncertain reverse skyline of a probabilistic product q retrieves all customers c ∈ C which include q as one of their preferred products. We present efficient pruning ideas and techniques for processing the uncertain reverse skyline query of a probabilistic product ...
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    Understanding the influence of a product is crucially important for making informed business decisions. This paper introduces a new type of skyline queries, called uncertain reverse skyline, for measuring the influence of a probabilistic product in uncertain data settings. More specifically, given a dataset of probabilistic products P and a set of customers C, an uncertain reverse skyline of a probabilistic product q retrieves all customers c ∈ C which include q as one of their preferred products. We present efficient pruning ideas and techniques for processing the uncertain reverse skyline query of a probabilistic product using R-Tree data index. We also present an efficient parallel approach to compute the uncertain reverse skyline and influence score of a probabilistic product. Our approach significantly outperforms the baseline approach derived from the existing literature. The efficiency of our approach is demonstrated by conducting experiments with both real and synthetic datasets.
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    Conference Title
    SSDBM 2017: 29TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT
    Volume
    Part F128636
    DOI
    https://doi.org/10.1145/3085504.3085508
    Copyright Statement
    © ACM 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 29th International Conference on Scientific and Statistical Database Management, ISBN: 978-1-4503-5282-6, 10.1145/3085504.3085508.
    Subject
    Data management and data science not elsewhere classified
    Data structures and algorithms
    Query processing and optimisation
    Publication URI
    http://hdl.handle.net/10072/346294
    Collection
    • Conference outputs

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