Patent application number | Description | Published |
20120239613 | GENERATING A PREDICTIVE MODEL FROM MULTIPLE DATA SOURCES - 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. | 09-20-2012 |
20120278275 | GENERATING A PREDICTIVE MODEL FROM MULTIPLE DATA SOURCES - 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. | 11-01-2012 |
20130006998 | INTERESTINGNESS OF DATA - Provided are techniques for analyzing fields. Statistical metrics for each field in a data set are received. A general interestingness index is generated for each field using one or more combination functions that aggregate standardized interestingness sub-indexes. One or more fields are identified as interesting for further analysis using the general interestingness index. One or more expert recommendations for field transformations are constructed for the identified one or more fields. | 01-03-2013 |
20130007003 | INTERESTINGNESS OF DATA - Provided are techniques for analyzing fields. Statistical metrics for each field in a data set are received. A general interestingness index is generated for each field using one or more combination functions that aggregate standardized interestingness sub-indexes. One or more fields are identified as interesting for further analysis using the general interestingness index. One or more expert recommendations for field transformations are constructed for the identified one or more fields. | 01-03-2013 |
20130218908 | COMPUTING AND APPLYING ORDER STATISTICS FOR DATA PREPARATION - 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. | 08-22-2013 |
20130218909 | COMPUTING AND APPLYING ORDER STATISTICS FOR DATA PREPARATION - 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. | 08-22-2013 |
20130226838 | MISSING VALUE IMPUTATION FOR PREDICTIVE MODELS - 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. | 08-29-2013 |
20130226842 | MISSING VALUE IMPUTATION FOR PREDICTIVE MODELS - 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. | 08-29-2013 |
20140032553 | RELATIONSHIP DISCOVERY IN BUSINESS ANALYTICS - 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. | 01-30-2014 |
20140032611 | RELATIONSHIP DISCOVERY IN BUSINESS ANALYTICS - 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. | 01-30-2014 |
20140114707 | INTERPRETATION OF STATISTICAL RESULTS - Provided are techniques for summarizing statistical results. Multiple sets of statistical results are received, wherein each of the sets of statistical results are ordered according to interestingness. Insights are generated based on the multiple sets of statistical results. Relationships between the generated insights are identified. An executive summary is generated with a set of findings based on the identified relationships. An interactive visualization is provided with the generated insights based on the executive summary. | 04-24-2014 |
20140201744 | COMPUTING REGRESSION MODELS - 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. | 07-17-2014 |
20140207722 | COMPUTING REGRESSION MODELS - 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. | 07-24-2014 |
20140258355 | INTERACTION DETECTION FOR GENERALIZED LINEAR MODELS - 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. | 09-11-2014 |
20140279775 | DECISION TREE INSIGHT DISCOVERY - Techniques for presenting insight into classification trees may include performing a grouping analysis to group leaf nodes of a classification tree into a significant group and an insignificant group, performing influential target category analysis to identify one or more influential target categories for the leaf nodes of the classification tree in the significant group, and presenting one or more insights into the classification tree based on the grouping analysis and the influential target category analysis. Techniques for presenting insight into regression trees may include performing a grouping analysis to group leaf nodes of a regression tree into a high group and a low group, performing unusual node detection analysis to detect one or more outlier nodes in the high group and in the low group, and presenting one or more insights into the regression tree based on the grouping analysis and the unusual node detection analysis | 09-18-2014 |
20150039624 | DECISION TREE INSIGHT DISCOVERY - Techniques for presenting insight into classification trees may include performing a grouping analysis to group leaf nodes of a classification tree into a significant group and an insignificant group, performing influential target category analysis to identify one or more influential target categories for the leaf nodes of the classification tree in the significant group, and presenting one or more insights into the classification tree based on the grouping analysis and the influential target category analysis. Techniques for presenting insight into regression trees may include performing a grouping analysis to group leaf nodes of a regression tree into a high group and a low group, performing unusual node detection analysis to detect one or more outlier nodes in the high group and in the low group, and presenting one or more insights into the regression tree based on the grouping analysis and the unusual node detection analysis | 02-05-2015 |