• Title/Summary/Keyword: Method of cumulative sums

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Maximizing the Overlay of Sample Units for Two Stratified Designs by Linear Programming

  • Ryu, Jea-Bok;Kim, Sun-Woong
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.719-729
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    • 2001
  • Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

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SPEC: Space Efficient Cubes for Data Warehouses (SPEC : 데이타 웨어하우스를 위한 저장 공간 효율적인 큐브)

  • Chun Seok-Ju;Lee Seok-Lyong;Kang Heum-Geun;Chung Chin-Wan
    • Journal of KIISE:Databases
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    • v.32 no.1
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    • pp.1-11
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    • 2005
  • An aggregation query computes aggregate information over a data cube in the query range specified by a user Existing methods based on the prefix-sum approach use an additional cube called the prefix-sum cube(PC), to store the cumulative sums of data, causing a high space overhead. This space overhead not only leads to extra costs for storage devices, but also causes additional propagations of updates and longer access time on physical devices. In this paper, we propose a new prefix-sum cube called 'SPEC' which drastically reduces the space of the PC in a large data warehouse. The SPEC decreases the update propagation caused by the dependency between values in cells of the PC. We develop an effective algorithm which finds dense sub-cubes from a large data cube. We perform an extensive experiment with respect to various dimensions of the data cube and query sizes, and examine the effectiveness and performance ot our proposed method. Experimental results show that the SPEC significantly reduces the space of the PC while maintaining a reasonable query performance.