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1. 发明授权

US08868573B2 Computing and applying order statistics for data preparation 有权
标题翻译：计算和应用订单统计数据进行准备
公开(公告)号：US08868573B2
公开(公告)日：2014-10-21
申请号：US13444718
申请日：2012-04-11
申请人： Yea J. Chu , Sier Han , Fan Li , Jing-Yun Shyr , Damir Spisic , Graham J. Wills , Jing Xu
发明人： Yea J. Chu , Sier Han , Fan Li , Jing-Yun Shyr , Damir Spisic , Graham J. Wills , Jing Xu
IPC分类号： G06F7/00
CPC分类号： G06F17/30283 , G06F17/3007 , G06Q10/10 , G06Q30/06
摘要： Provided are techniques for generating order statistics and error bounds. For each of multiple, distributed data sources, a finite number of data bins are created for each field in that data source. Data values in each of the multiple, distributed data sources are processed to generate basic summaries for each of the data bins in a single pass of the data values. The data bins from each of the multiple, distributed data sources are sorted. One or more approximate order statistics are computed for a data set by accumulating counts from a number of the sorted data bins. Lower and upper error bounds are provided for each of the computed one or more approximate order statistics, wherein the lower and upper error bounds are values delimiting an interval containing a true value of an order statistic.
摘要翻译：提供了用于生成订单统计和错误界限的技术。对于多个分布式数据源中的每一个，为数据源中的每个字段创建有限数量的数据仓。处理多个分布式数据源中的每一个中的数据值，以便在单次数据值中为每个数据仓生成基本摘要。来自多个分布式数据源中的每一个的数据仓被排序。通过从多个排序的数据仓中累积计数，为数据集计算一个或多个近似顺序统计量。为所计算的一个或多个近似秩统计中的每一个提供下限和上限误差界限，其中下限误差界限和上限误差界限是定义包含订单统计量的真实值的间隔的值。

2. 发明申请

US20130218908A1 COMPUTING AND APPLYING ORDER STATISTICS FOR DATA PREPARATION 审中-公开
标题翻译：计算和应用订单统计数据准备
公开(公告)号：US20130218908A1
公开(公告)日：2013-08-22
申请号：US13399838
申请日：2012-02-17
申请人： Yea J. Chu , Sier Han , Fan Li , Jing-Yun Shyr , Damir Spisic , Graham J. Wills , Jing Xu
发明人： Yea J. Chu , Sier Han , Fan Li , Jing-Yun Shyr , Damir Spisic , Graham J. Wills , Jing Xu
IPC分类号： G06F17/30
CPC分类号： G06Q30/06 , G06F16/11 , G06F16/27 , G06Q10/10
摘要： Provided are techniques for generating order statistics and error bounds. For each of multiple, distributed data sources, a finite number of data bins are created for each field in that data source. Data values in each of the multiple, distributed data sources are processed to generate basic summaries for each of the data bins in a single pass of the data values. The data bins from each of the multiple, distributed data sources are sorted. One or more approximate order statistics are computed for a data set by accumulating counts from a number of the sorted data bins. Lower and upper error bounds are provided for each of the computed one or more approximate order statistics, wherein the lower and upper error bounds are values delimiting an interval containing a true value of an order statistic.
摘要翻译：提供了用于生成订单统计和错误界限的技术。对于多个分布式数据源中的每一个，为数据源中的每个字段创建有限数量的数据仓。处理多个分布式数据源中的每一个中的数据值，以便在单次数据值中为每个数据仓生成基本摘要。来自多个分布式数据源中的每一个的数据仓被排序。通过从多个排序的数据仓中累积计数，为数据集计算一个或多个近似顺序统计量。为所计算的一个或多个近似秩统计中的每一个提供下限和上限误差界限，其中下限误差界限和上限误差界限是定义包含订单统计量的真实值的间隔的值。

3. 发明申请

US20130218909A1 COMPUTING AND APPLYING ORDER STATISTICS FOR DATA PREPARATION 有权
公开(公告)号：US20130218909A1
公开(公告)日：2013-08-22
申请号：US13444718
申请日：2012-04-11
申请人： Yea J. Chu , Sier Han , Fan Li , Jing-Yun Shyr , Damir Spisic , Graham J. Wills , Jing Xu
发明人： Yea J. Chu , Sier Han , Fan Li , Jing-Yun Shyr , Damir Spisic , Graham J. Wills , Jing Xu
IPC分类号： G06F17/30
CPC分类号： G06F17/30283 , G06F17/3007 , G06Q10/10 , G06Q30/06
摘要： Provided are techniques for generating order statistics and error bounds. For each of multiple, distributed data sources, a finite number of data bins are created for each field in that data source. Data values in each of the multiple, distributed data sources are processed to generate basic summaries for each of the data bins in a single pass of the data values. The data bins from each of the multiple, distributed data sources are sorted. One or more approximate order statistics are computed for a data set by accumulating counts from a number of the sorted data bins. Lower and upper error bounds are provided for each of the computed one or more approximate order statistics, wherein the lower and upper error bounds are values delimiting an interval containing a true value of an order statistic.

4. 发明授权

US08965895B2 Relationship discovery in business analytics 有权
公开(公告)号：US08965895B2
公开(公告)日：2015-02-24
申请号：US13562201
申请日：2012-07-30
申请人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Damir Spisic , Xueying Zhang
发明人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Damir Spisic , Xueying Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30598 , G06F17/30592
摘要： A subset of (k−1)-dimensional tables are received, wherein k is greater than 1. A set of k-dimensional tables is created by combining each of the (k−1)-dimensional tables with a non-included dimension corresponding to a 1-dimensional table. Significance of interaction and interaction effect size is computed for the created set of k-dimensional tables to determine dimension and measure interactions.

5. 发明申请

US20140032553A1 RELATIONSHIP DISCOVERY IN BUSINESS ANALYTICS 有权
标题翻译：业务分析中的关系发现
公开(公告)号：US20140032553A1
公开(公告)日：2014-01-30
申请号：US13562201
申请日：2012-07-30
申请人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Damir Spisic , Xueying Zhang
发明人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Damir Spisic , Xueying Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30598 , G06F17/30592
摘要： A subset of (k−1)-dimensional tables are received, wherein k is greater than 1. A set of k-dimensional tables is created by combining each of the (k−1)-dimensional tables with a non-included dimension corresponding to a 1-dimensional table. Significance of interaction and interaction effect size is computed for the created set of k-dimensional tables to determine dimension and measure interactions.
摘要翻译：接收（k-1）维表的子集，其中k大于1.通过将（k-1）维表中的每一个与对应的不包括的维度组合来创建一组k维表到一维表。对于创建的k维表集合计算交互和交互效应大小的意义，以确定维度和测量相互作用。

6. 发明授权

US08843423B2 Missing value imputation for predictive models 有权
标题翻译：预测模型缺失值插补
公开(公告)号：US08843423B2
公开(公告)日：2014-09-23
申请号：US13403863
申请日：2012-02-23
申请人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Jing Xu
发明人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Jing Xu
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06N5/025 , G06F15/18 , G06N5/04 , G06N99/005
摘要： Provided are techniques for imputing a missing value for each of one or more predictor variables. Data is received from one or more data sources. For each of the one or more predictor variables, an imputation model is built based on information of a target variable; a type of imputation model to construct is determined based on the one or more data sources, a measurement level of the predictor variable, and a measurement level of the target variable; and the determined type of imputation model is constructed using basic statistics of the predictor variable and the target variable. The missing value is imputed for each of the one or more predictor variables using the data from the one or more data sources and one or more built imputation models to generate a completed data set.
摘要翻译：提供了用于估算每个一个或多个预测变量的缺失值的技术。从一个或多个数据源接收数据。对于一个或多个预测变量中的每一个，基于目标变量的信息构建插补模型; 基于一个或多个数据源，预测变量的测量水平和目标变量的测量水平来确定构造的插补模型的类型; 并使用预测变量和目标变量的基本统计量构建确定的插补模型。使用来自一个或多个数据源的数据和一个或多个内置插补模型来为每个一个或多个预测变量估计缺失值，以生成完成的数据集。

7. 发明授权

US09443194B2 Missing value imputation for predictive models 有权
标题翻译：预测模型缺失值插补
公开(公告)号：US09443194B2
公开(公告)日：2016-09-13
申请号：US13445796
申请日：2012-04-12
申请人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Jing Xu
发明人： Yea J. Chu , Sier Han , Jing-Yun Shyr , Jing Xu
IPC分类号： G06N5/04 , G06N5/02 , G06N99/00 , G06F15/18
CPC分类号： G06N5/025 , G06F15/18 , G06N5/04 , G06N99/005
摘要： Provided are techniques for imputing a missing value for each of one or more predictor variables. Data is received from one or more data sources. For each of the one or more predictor variables, an imputation model is built based on information of a target variable; a type of imputation model to construct is determined based on the one or more data sources, a measurement level of the predictor variable, and a measurement level of the target variable; and the determined type of imputation model is constructed using basic statistics of the predictor variable and the target variable. The missing value is imputed for each of the one or more predictor variables using the data from the one or more data sources and one or more built imputation models to generate a completed data set.
摘要翻译：提供了用于估算每个一个或多个预测变量的缺失值的技术。从一个或多个数据源接收数据。对于一个或多个预测变量中的每一个，基于目标变量的信息构建插补模型; 基于一个或多个数据源，预测变量的测量水平和目标变量的测量水平来确定构造的插补模型的类型; 并使用预测变量和目标变量的基本统计量构建确定的插补模型。使用来自一个或多个数据源的数据和一个或多个内置插补模型来为每个一个或多个预测变量估计缺失值，以生成完成的数据集。

8. 发明授权

US08990149B2 Generating a predictive model from multiple data sources 有权
公开(公告)号：US08990149B2
公开(公告)日：2015-03-24
申请号：US13048536
申请日：2011-03-15
申请人： Marius I. Danciu , Fan Li , Michael McRoberts , Jing-Yun Shyr , Damir Spisic , Jing Xu
发明人： Marius I. Danciu , Fan Li , Michael McRoberts , Jing-Yun Shyr , Damir Spisic , Jing Xu
IPC分类号： G06F7/00 , G06F17/00 , G06Q10/06
CPC分类号： G06Q10/06
摘要： Techniques are disclosed for generating an ensemble model from multiple data sources. In one embodiment, the ensemble model is generated using a global validation sample, a global holdout sample and base models generated from the multiple data sources. An accuracy value may be determined for each base model, on the basis of the global validation dataset. The ensemble model may be generated from a subset of the base models, where the subset is selected on the basis of the determined accuracy values.

9. 发明申请

US20120239613A1 GENERATING A PREDICTIVE MODEL FROM MULTIPLE DATA SOURCES 有权
标题翻译：从多个数据源生成预测模型
公开(公告)号：US20120239613A1
公开(公告)日：2012-09-20
申请号：US13048536
申请日：2011-03-15
申请人： Marius I. Danciu , Fan Li , Michael McRoberts , Jing-Yun Shyr , Damir Spisic , Jing Xu
发明人： Marius I. Danciu , Fan Li , Michael McRoberts , Jing-Yun Shyr , Damir Spisic , Jing Xu
IPC分类号： G06F7/00 , G06F17/00 , G06F17/30
CPC分类号： G06Q10/06
摘要： Techniques are disclosed for generating an ensemble model from multiple data sources. In one embodiment, the ensemble model is generated using a global validation sample, a global holdout sample and base models generated from the multiple data sources. An accuracy value may be determined for each base model, on the basis of the global validation dataset. The ensemble model may be generated from a subset of the base models, where the subset is selected on the basis of the determined accuracy values.
摘要翻译：公开了用于从多个数据源生成集合模型的技术。在一个实施例中，使用全局验证样本，全局保持样本和从多个数据源生成的基本模型来生成集合模型。可以基于全局验证数据集为每个基本模型确定精度值。集合模型可以从基本模型的子集生成，其中基于确定的精度值选择子集。

10. 发明授权

US08996452B2 Generating a predictive model from multiple data sources 有权
标题翻译：从多个数据源生成预测模型
公开(公告)号：US08996452B2
公开(公告)日：2015-03-31
申请号：US13545817
申请日：2012-07-10
申请人： Marius I. Danciu , Fan Li , Michael McRoberts , Jing-Yun Shyr , Damir Spisic , Jing Xu
发明人： Marius I. Danciu , Fan Li , Michael McRoberts , Jing-Yun Shyr , Damir Spisic , Jing Xu
IPC分类号： G06F7/00 , G06F17/00 , G06Q10/06
CPC分类号： G06Q10/06
摘要： Techniques are disclosed for generating an ensemble model from multiple data sources. In one embodiment, the ensemble model is generated using a global validation sample, a global holdout sample and base models generated from the multiple data sources. An accuracy value may be determined for each base model, on the basis of the global validation dataset. The ensemble model may be generated from a subset of the base models, where the subset is selected on the basis of the determined accuracy values.
摘要翻译：公开了用于从多个数据源生成集合模型的技术。在一个实施例中，使用全局验证样本，全局保持样本和从多个数据源生成的基本模型来生成集合模型。可以基于全局验证数据集为每个基本模型确定精度值。集合模型可以从基本模型的子集生成，其中基于确定的精度值选择子集。

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