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    • 1. 发明申请
    • GENERATING HISTOGRAMS OF POPULATION DATA BY SCALING FROM SAMPLE DATA
    • 通过从样本数据中分类生成人口统计数据
    • US20080059125A1
    • 2008-03-06
    • US11469855
    • 2006-09-02
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • G06F19/00G06F17/40G06F17/18
    • G06F17/18G06F17/30469
    • Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate. The resulting estimate is then used when scaling, which can take domain knowledge of the data being scaled into account in order to prevent scaled estimates from exceeding the limits of the domain Also, a “sum of the parts” mathematical relationship can be taken into account during scaling that the sum of the scaled distinct values for each bin of an estimate histogram should total an estimate for the total distinct values of the entire population.
    • 基于群体样本形成的直方图,例如从数据存储的随机页面级样本创建的直方图,被智能地缩放到估计数据存储的整个群体的分布的直方图。 作为可选优化,在页级采样期间观察到重复样本的阈值数量时,总体总体数据中不同值的数量被推定为样本数据中不同值的数量。 此外,在估计总体人口的不同价值时,可以可选地将“超”估计量用作估计的下限。 然后在缩放时使用所得到的估计,这可以将数据的领域知识考虑到考虑中,以防止按比例估计超出域的限制。另外,可以考虑“部件之和”数学关系 在缩放期间,估计直方图的每个bin的缩放的不同值的总和应该对整个群体的总不同值的总和。
    • 2. 发明申请
    • GENERATING HISTOGRAMS OF POPULATION DATA BY SCALING FROM SAMPLE DATA
    • 通过从样本数据中分类生成人口统计数据
    • US20100138407A1
    • 2010-06-03
    • US12700274
    • 2010-02-04
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • G06F15/18G06F17/30
    • G06F17/18G06F17/30469
    • Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate. The resulting estimate is then used when scaling, which can take domain knowledge of the data being scaled into account in order to prevent scaled estimates from exceeding the limits of the domain. Also, a “sum of the parts” mathematical relationship can be taken into account during scaling that the sum of the scaled distinct values for each bin of an estimate histogram should total an estimate for the total distinct values of the entire population.
    • 基于群体样本形成的直方图,例如从数据存储的随机页面级样本创建的直方图,被智能地缩放到估计数据存储的整个群体的分布的直方图。 作为可选优化,在页级采样期间观察到重复样本的阈值数量时,总体总体数据中不同值的数量被推定为样本数据中不同值的数量。 此外,在估计总体人口的不同价值时,可以可选地将“超”估计量用作估计的下限。 然后在缩放时使用所得到的估计,这可以将数据的领域知识考虑到考虑中,以防止定标估计超出域的限制。 此外,在缩放期间可以考虑“部分之和”数学关系,即估计直方图的每个仓的缩放的不同值的总和应该对整个群体的总不同值的总和。
    • 3. 发明授权
    • Generating histograms of population data by scaling from sample data
    • 通过从样本数据缩放生成填充数据的直方图
    • US07707005B2
    • 2010-04-27
    • US11469855
    • 2006-09-02
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • G06F19/00G06F17/40G06F17/18
    • G06F17/18G06F17/30469
    • Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate. The resulting estimate is then used when scaling, which can take domain knowledge of the data being scaled into account in order to prevent scaled estimates from exceeding the limits of the domain. Also, a “sum of the parts” mathematical relationship can be taken into account during scaling that the sum of the scaled distinct values for each bin of an estimate histogram should total an estimate for the total distinct values of the entire population.
    • 基于群体样本形成的直方图,例如从数据存储的随机页面级样本创建的直方图,被智能地缩放到估计数据存储的整个群体的分布的直方图。 作为可选优化,在页级采样期间观察到重复样本的阈值数量时,总体总体数据中不同值的数量被推定为样本数据中不同值的数量。 此外,在估计总体人口的不同价值时,可以可选地将“超”估计量用作估计的下限。 然后在缩放时使用所得到的估计,这可以将数据的领域知识考虑到考虑中,以防止定标估计超出域的限制。 此外,在缩放期间可以考虑“部分之和”数学关系,即估计直方图的每个仓的缩放的不同值的总和应该对整个群体的总不同值的总和。
    • 4. 发明授权
    • Generating histograms of population data by scaling from sample data
    • 通过从样本数据缩放生成填充数据的直方图
    • US08316009B2
    • 2012-11-20
    • US12700274
    • 2010-02-04
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • Campbell Bryce FraserIan JosePeter Alfred Zabback
    • G06F17/30
    • G06F17/18G06F17/30469
    • Histograms formed based on samples of a population, such as histograms created from random page-level samples of a data store, are intelligently scaled to histograms estimating distribution of the entire population of the data store. As an optional optimization, where a threshold number of duplicate samples are observed during page-level sampling, the number of distinct values in the overall population data is presumed to be the number of distinct values in the sample data. Also, during estimation of distinct values of an overall population, a “Chao” estimator can optionally be utilized as a lower bound of the estimate. The resulting estimate is then used when scaling, which can take domain knowledge of the data being scaled into account in order to prevent scaled estimates from exceeding the limits of the domain. Also, a “sum of the parts” mathematical relationship can be taken into account during scaling that the sum of the scaled distinct values for each bin of an estimate histogram should total an estimate for the total distinct values of the entire population.
    • 基于群体样本形成的直方图,例如从数据存储的随机页面级样本创建的直方图,被智能地缩放到估计数据存储的整个群体的分布的直方图。 作为可选优化,在页级采样期间观察到重复样本的阈值数量时,总体总体数据中不同值的数量被推定为样本数据中不同值的数量。 此外,在估计总体人口的不同值时,可以可选地将Chao估计量用作估计的下限。 然后在缩放时使用所得到的估计,这可以将数据的领域知识考虑到考虑中,以防止定标估计超出域的限制。 此外,在缩放期间可以考虑到部分数学关系的总和,即估计直方图的每个bin的缩放的不同值的总和应该对于整个群体的总不同值的总和。