Patents by Inventor Yea J. Chu
Yea J. Chu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 9443194Abstract: 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.Type: GrantFiled: April 12, 2012Date of Patent: September 13, 2016Assignee: International Business Machines CorporationInventors: Yea J. Chu, Sier Han, Jing-Yun Shyr, Jing Xu
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Patent number: 9361274Abstract: Provided are techniques for interaction detection for generalized linear models. Basic statistics are calculated for a pair of categorical predictor variables and a target variable from a dataset during a single pass over the dataset. It is determined whether there is a significant interaction effect for the pair of categorical predictor variables on the target variable by: calculating a log-likelihood value for a full generalized linear model without estimating model parameters; calculating the model parameters for a reduced generalized linear model with a recursive marginal mean accumulation technique using the basic statistics; calculating a log-likelihood value for the reduced generalized linear model; calculating a likelihood ratio test statistic using the log-likelihood value for the full generalized linear model and the log-likelihood value for the reduced generalized linear model; calculating a p-value of the likelihood ratio test statistic; and comparing the p-value to a significance level.Type: GrantFiled: March 11, 2013Date of Patent: June 7, 2016Assignee: International Business Machines CorporationInventors: Yea J. Chu, Sier Han, Jing-Yun Shyr
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Patent number: 9159028Abstract: Provided are techniques for computing a task result. A processing data set of records is created, wherein each of the records contains data specific to a sub-task from a set of actual sub-tasks and contains a reference to data shared by the set of actual sub-tasks, and wherein a number of the records is equivalent to a number of the actual sub-tasks in the set of actual sub-tasks. With each mapper in a set of mappers, one of the records of the processing data set is received and an assigned sub-task is executed using the received one of the records to generate output. With a single reducer, the output from each mapper in the set of mappers is reduced to determine a task result.Type: GrantFiled: January 11, 2013Date of Patent: October 13, 2015Assignee: International Business Machines CorporationInventors: Yea J. Chu, Dong Liang, Jing-Yun Shyr
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Patent number: 9152921Abstract: Provided are techniques for computing a task result. A processing data set of records is created, wherein each of the records contains data specific to a sub-task from a set of actual sub-tasks and contains a reference to data shared by the set of actual sub-tasks, and wherein a number of the records is equivalent to a number of the actual sub-tasks in the set of actual sub-tasks. With each mapper in a set of mappers, one of the records of the processing data set is received and an assigned sub-task is executed using the received one of the records to generate output. With a single reducer, the output from each mapper in the set of mappers is reduced to determine a task result.Type: GrantFiled: March 21, 2014Date of Patent: October 6, 2015Assignee: International Business Machines CorporationInventors: Yea J. Chu, Dong Liang, Jing-Yun Shyr
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Patent number: 9053170Abstract: 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.Type: GrantFiled: March 8, 2013Date of Patent: June 9, 2015Assignee: International Business Machines CorporationInventors: Yea J. Chu, Sier Han, Jing-Yun Shyr, Damir Spisic, Xueying Zhang
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Patent number: 8965895Abstract: 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.Type: GrantFiled: July 30, 2012Date of Patent: February 24, 2015Assignee: International Business Machines CorporationInventors: Yea J. Chu, Sier Han, Jing-Yun Shyr, Damir Spisic, Xueying Zhang
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Publication number: 20150006605Abstract: Provided are techniques for interaction detection for generalized linear models. Basic statistics are calculated for a pair of categorical predictor variables and a target variable from a dataset during a single pass over the dataset. It is determined whether there is a significant interaction effect for the pair of categorical predictor variables on the target variable by: calculating a log-likelihood value for a full generalized linear model without estimating model parameters; calculating the model parameters for a reduced generalized linear model with a recursive marginal mean accumulation technique using the basic statistics; calculating a log-likelihood value for the reduced generalized linear model; calculating a likelihood ratio test statistic using the log-likelihood value for the full generalized linear model and the log-likelihood value for the reduced generalized linear model; calculating a p-value of the likelihood ratio test statistic; and comparing the p-value to a significance level.Type: ApplicationFiled: September 15, 2014Publication date: January 1, 2015Inventors: Yea J. Chu, Sier Han, Jing-Yun Shyr
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Patent number: 8868573Abstract: 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.Type: GrantFiled: April 11, 2012Date of Patent: October 21, 2014Assignee: International Business Machines CorporationInventors: Yea J. Chu, Sier Han, Fan Li, Jing-Yun Shyr, Damir Spisic, Graham J. Wills, Jing Xu
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Patent number: 8843423Abstract: 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.Type: GrantFiled: February 23, 2012Date of Patent: September 23, 2014Assignee: International Business Machines CorporationInventors: Yea J. Chu, Sier Han, Jing-Yun Shyr, Jing Xu
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Publication number: 20140258355Abstract: Provided are techniques for interaction detection for generalized linear models. Basic statistics are calculated for a pair of categorical predictor variables and a target variable from a dataset during a single pass over the dataset. It is determined whether there is a significant interaction effect for the pair of categorical predictor variables on the target variable by: calculating a log-likelihood value for a full generalized linear model without estimating model parameters; calculating the model parameters for a reduced generalized linear model with a recursive marginal mean accumulation technique using the basic statistics; calculating a log-likelihood value for the reduced generalized linear model; calculating a likelihood ratio test statistic using the log-likelihood value for the full generalized linear model and the log-likelihood value for the reduced generalized linear model; calculating a p-value of the likelihood ratio test statistic; and comparing the p-value to a significance level.Type: ApplicationFiled: March 11, 2013Publication date: September 11, 2014Applicant: International Business Machines CorporationInventors: Yea J. Chu, Sier Han, Jing-Yun Shyr
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Publication number: 20140207722Abstract: Provided are techniques for computing a task result. A processing data set of records is created, wherein each of the records contains data specific to a sub-task from a set of actual sub-tasks and contains a reference to data shared by the set of actual sub-tasks, and wherein a number of the records is equivalent to a number of the actual sub-tasks in the set of actual sub-tasks. With each mapper in a set of mappers, one of the records of the processing data set is received and an assigned sub-task is executed using the received one of the records to generate output. With a single reducer, the output from each mapper in the set of mappers is reduced to determine a task result.Type: ApplicationFiled: March 21, 2014Publication date: July 24, 2014Applicant: International Business Machines CorporationInventors: Yea J. Chu, Dong Liang, Jing-Yun Shyr
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Publication number: 20140201744Abstract: Provided are techniques for computing a task result. A processing data set of records is created, wherein each of the records contains data specific to a sub-task from a set of actual sub-tasks and contains a reference to data shared by the set of actual sub-tasks, and wherein a number of the records is equivalent to a number of the actual sub-tasks in the set of actual sub-tasks. With each mapper in a set of mappers, one of the records of the processing data set is received and an assigned sub-task is executed using the received one of the records to generate output. With a single reducer, the output from each mapper in the set of mappers is reduced to determine a task result.Type: ApplicationFiled: January 11, 2013Publication date: July 17, 2014Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yea J. Chu, Dong Liang, Jing-Yun Shyr
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Publication number: 20140032553Abstract: 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.Type: ApplicationFiled: July 30, 2012Publication date: January 30, 2014Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yea J. Chu, Sier Han, Jing-Yun Shyr, Damir Spisic, Xueying Zhang
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Publication number: 20130226838Abstract: 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.Type: ApplicationFiled: February 23, 2012Publication date: August 29, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yea J. CHU, Sier HAN, Jing-Yun SHYR, Jing XU
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Publication number: 20130226842Abstract: 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.Type: ApplicationFiled: April 12, 2012Publication date: August 29, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yea J. CHU, Sier HAN, Jing-Yun SHYR, Jing XU
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Publication number: 20130218908Abstract: 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.Type: ApplicationFiled: February 17, 2012Publication date: August 22, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yea J. Chu, Sier Han, Fan Li, Jing-Yun Shyr, Damir Spisic, Graham J. Wills, Jing Xu
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Publication number: 20130218909Abstract: 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.Type: ApplicationFiled: April 11, 2012Publication date: August 22, 2013Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Yea J. Chu, Sier Han, Fan Li, Jing-Yun Shyr, Damir Spisic, Graham J. Wills, Jing Xu