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    An effective technique and practical utility for approximate query processing

    238877_Inoue 2016.pdf (2.529Mb)
    Access Status
    Open access
    Authors
    Inoue, Tomohiro
    Date
    2015
    Supervisor
    Dr Raj Gopalan
    Dr Aneesh Krishna
    Type
    Thesis
    Award
    MPhil
    
    Metadata
    Show full item record
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/417
    Collection
    • Curtin Theses
    Abstract

    This dissertation studies efficient and effective approximate query processing for decision support systems. A novel method that enables fast query processing and reliable approximation even in highly selective queries is proposed and evaluated. Also, utility software that enables the implementation of the proposed method in databases and enables the execution of approximate query processing in SQL is developed as part of this research.

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    • Approximate Query Processing on High Dimensionality Database Tables Using Multidimensional Cluster Sampling View
      Inoue, T.; Krishna, Aneesh; Gopalan, Raj (2016)
      Approximate query processing based on random sampling is one of the most useful methods for the efficient computation of large quantities of data kept in databases. However, small samples obtained through random sampling ...
    • Estimating Sufficient Sample Sizes for Approximate Decision Support Queries
      Rudra, Amit; Gopalan, Raj; Achuthan, Narasimaha (2014)
      Sampling schemes for approximate processing of highly selective decision support queries need to retrieve sufficient number of records that can provide reliable results within acceptable error limits. The k-MDI tree is ...
    • Selecting adequate samples for approximate decision support queries
      Rudra, Amit; Gopalan, Raj; Achuthan, Narasimaha (2013)
      For highly selective queries, a simple random sample of records drawn from a large data warehouse may not contain sufficient number of records that satisfy the query conditions. Efficient sampling schemes for such queries ...
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