Patent application number | Description | Published |
20090030864 | METHOD FOR CONSTRUCTING SEGMENTATION-BASED PREDICTIVE MODELS - The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies. | 01-29-2009 |
20090077011 | SYSTEM AND METHOD FOR EXECUTING COMPUTE-INTENSIVE DATABASE USER-DEFINED PROGRAMS ON AN ATTACHED HIGH-PERFORMANCE PARALLEL COMPUTER - The invention pertains to a system and method for dispatching and executing the compute-intensive parts of the workflow for database queries on an attached high-performance, parallel computing platform. The performance overhead for moving the required data and results between the database platform and the high-performance computing platform where the workload is executed is amortized in several ways, for example,
| 03-19-2009 |
20120011518 | SHARING WITH PERFORMANCE ISOLATION BETWEEN TENANTS IN A SOFTWARE-AS-A SERVICE SYSTEM - An apparatus hosting a multi-tenant software-as-a-service (SaaS) system maximizes resource sharing capability of the SaaS system. The apparatus receives service requests from multiple users belonging to different tenants of the multi-tenant SaaS system. The apparatus partitions the resources in the SaaS system into different resource groups. Each resource group handles a category of the service requests. The apparatus estimates costs of the service requests of the users. The apparatus dispatches service requests to resource groups according to the estimated costs, whereby the resources are shared, among the users, without impacting each other. | 01-12-2012 |
20120303410 | DEMAND MODELING IN RETAIL CATEGORIES USING RETAIL SALES DATA SETS WITH MISSING DATA ELEMENTS - A system, method and computer program product provides for accurate demand modeling and forecasting in retail categories using retail sales data sets with missing data values, in order to enable a variety of retail decision-support applications. | 11-29-2012 |
20120303411 | DEMAND MODELING AND PREDICTION IN A RETAIL CATEGORY - System, method and computer program product for demand modeling and prediction in retail categories. The method uses time-series data comprising of unit prices and unit sales for a designated choice set of related products, with the time-series data obtained over a given sequence of sales reporting periods, and over a collection of stores in a market geography. Other relevant data sets from participating retail entities that include additional product attribute data such as market and consumer factors that affect retail demand are further used. A demand model for improved accuracy is achieved by individual sub-modeling method steps of: estimating a model for price movements and price dynamics from the time series data of unit-prices in the aggregated sales data; estimating a model for market share of each product in the retail category using the aggregated sales data and integrated additional product attribute data; and, estimating generating a model for an overall market demand in the retail category from the aggregated sales data. | 11-29-2012 |
20130036082 | MULTIPLE IMPUTATION OF MISSING DATA IN MULTI-DIMENSIONAL RETAIL SALES DATA SETS VIA TENSOR FACTORIZATION - A system, method and computer program product provides for multiple imputation of missing data elements in retail data sets used for modeling and decision-support applications based on the multi-dimensional, tensor structure of the data sets, and a fast, scalable scheme is implemented that is suitable for large data sets. The method generates multiple imputations comprising a set of complete data sets each containing one of a plurality of imputed realizations for the missing data values in the original data set, so that the variability in the magnitudes of these missing data values can be captured for subsequent statistical analysis. The method is based on the multi-dimensional structure of the retail data sets incorporating tensor factorization, that in a preferred embodiment can be implemented using fast, scalable imputation methods suitable for large data sets, to obtain multiple complete data sets in which the original missing values are replaced by various imputed values. | 02-07-2013 |
20140257832 | IDENTIFYING POTENTIAL AUDIT TARGETS IN FRAUD AND ABUSE INVESTIGATIONS - Detecting fraud in the health care industry includes selecting a given focus scenario (e.g., prescription rate in a certain drug therapeutic class) for audit analysis, and constructing baseline models with the appropriate normalizations to describe the expected behavior within the focus area. These baseline models are then used, in conjunction with statistical hypothesis testing, to identify entities whose behavior diverges significantly from their expected behavior according to the baseline models. A Likelihood Ratio (LR) score over the relevant claims with respect to the baseline model is obtained for each entity, and the p-value significance of this score is evaluated to ensure that the abnormal behavior can be identified at the specified level of statistical significance. The approach may be used as part of a preliminary computer-aided audit process in which the relevant entities with the abnormal behavior are identified with high selectivity for a subsequent human-intensive audit investigation. | 09-11-2014 |
20140257846 | IDENTIFYING POTENTIAL AUDIT TARGETS IN FRAUD AND ABUSE INVESTIGATIONS - Detecting fraud in the health care industry includes selecting a given focus scenario (e.g., prescription rate in a certain drug therapeutic class) for audit analysis, and constructing baseline models with the appropriate normalizations to describe the expected behavior within the focus area. These baseline models are then used, in conjunction with statistical hypothesis testing, to identify entities whose behavior diverges significantly from their expected behavior according to the baseline models. A Likelihood Ratio (LR) score over the relevant claims with respect to the baseline model is obtained for each entity, and the p-value significance of this score is evaluated to ensure that the abnormal behavior can be identified at the specified level of statistical significance. The approach may be used as part of a preliminary computer-aided audit process in which the relevant entities with the abnormal behavior are identified with high selectivity for a subsequent human-intensive audit investigation. | 09-11-2014 |
20140324532 | SYSTEM AND METHOD FOR MODELING AND FORECASTING CYCLICAL DEMAND SYSTEMS WITH DYNAMIC CONTROLS AND DYNAMIC INCENTIVES - Systems and methods for modeling and forecasting cyclical demand systems in the presence of dynamic control or dynamic incentives. A method for modeling a cyclical demand system comprises obtaining historical data on one or more demand measurements over a plurality of demand cycles, obtaining historical data on incentive signals over the plurality of demand cycles, constructing a model using the obtained historical data on the one or more demand measurements and the incentive signals, wherein constructing the model comprises specifying a state-space model, specifying variance parameters in the model, and estimating unknown variance parameters. | 10-30-2014 |
20140365022 | Managing Time-Substitutable Electricity Usage using Dynamic Controls - A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives. | 12-11-2014 |
20140365024 | Managing Time-Substitutable Electricity Usage using Dynamic Controls - A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives. | 12-11-2014 |
20140365276 | DATA-DRIVEN INVENTORY AND REVENUE OPTIMIZATION FOR UNCERTAIN DEMAND DRIVEN BY MULTIPLE FACTORS - Based on a time series history of a random variable representing demand for at least one of a good and a service as a function of at least one controllable demand driver, obtain a quantile regression function that estimates a quantile of a demand distribution function; obtain a mixed- and/or super-quantile regression function that estimates conditional value at risk; and obtain a regression function that estimates mean of the demand distribution function. Joint optimization of: inventory of the at least one of a good and a service, and the at least one controllable demand driver, is undertaken based on the quantile regression function and the mixed- and/or super-quantile regression function, to obtain an optimal value for the at least one controllable demand driver and an implied optimal value for a stocking level. One or more exogenous demand drivers can optionally be taken into account. | 12-11-2014 |
20150019289 | System and Method for Forecasting Prices of Frequently-Promoted Retail Products - Systems and methods for forecasting prices of products are provided. A method for forecasting prices of products, comprises obtaining a time series history of a price of a product, imputing a state indicator value for each price data from the time series history, wherein a state is one of a promotional price state and a regular price state, extracting a first price time series for the price data in the promotional state and a second price time series for the price data in the regular state, extracting a promotion duration time series from the time series history, obtaining respective point forecasts for the extracted first price time series, the second price time series and the promotion duration time series, and combining the point forecasts for the extracted first and second price time series and the promotion duration time series to obtain a final price forecast. | 01-15-2015 |
20150019295 | SYSTEM AND METHOD FOR FORECASTING PRICES OF FREQUENTLY- PROMOTED RETAIL PRODUCTS - Systems and methods for forecasting prices of products are provided. A method for forecasting prices of products, comprises obtaining a time series history of a price of a product, imputing a state indicator value for each price data from the time series history, wherein a state is one of a promotional price state and a regular price state, extracting a first price time series for the price data in the promotional state and a second price time series for the price data in the regular state, extracting a promotion duration time series from the time series history, obtaining respective point forecasts for the extracted first price time series, the second price time series and the promotion duration time series, and combining the point forecasts for the extracted first and second price time series and the promotion duration time series to obtain a final price forecast. | 01-15-2015 |