Abstract: 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.

Abstract: Software that performs the following steps: (i) receiving a first set of observed data pertaining to healthcare events, the first set of observed data including a subset of patient care event data pertaining to patient care events and a subset of prescription data pertaining to prescription events; (ii) generating a graphical model representing a probabilistic relationship between the patient care event data and the prescription data, the graphical model including a set of latent variable(s) estimated from the first set of observed data using an expectation maximization method; (iii) receiving a second set of observed data pertaining to healthcare events associated with a healthcare provider; and (iv) computing, using a dynamic programming approach, a first prescription score for the healthcare provider relating to a computed probability under the generated graphical model of at least one prescription event of the second set of observed data.

Abstract: This is a method and devices used to invite a user from one message chat service into a preexisting group chat session located on another chat service.

Abstract: The invention pertains to a system and method for a set of middleware components for supporting the execution of computational applications on high-performance computing platform. A specific embodiment of this invention was used to deploy a financial risk application on Blue Gene/L parallel supercomputer. The invention is relevant to any application where the input and output data are stored in external sources, such as SQL databases, where the automatic pre-staging and post-staging of the data between the external data sources and the computational platform is desirable. This middleware provides a number of core features to support these applications including for example, an automated data extraction and staging gateway, a standardized high-level job specification schema, a well-defined web services (SOAP) API for interoperability with other applications, and a secure HTML/JSP web-based interface suitable for non-expert and non-privileged users.

Abstract: The computer creates a utility demand forecast model for weather parameters by receiving a plurality of utility parameter values, wherein each received utility parameter value corresponds to a weather parameter value. Determining that a range of weather parameter values lacks a sufficient amount of corresponding received utility parameter values. Determining one or more utility parameter values that corresponds to the range of weather parameter values. Creating a model which correlates the received and the determined utility parameter values with the corresponding weather parameters values.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This is a method and devices used to invite a user from one message chat service into a preexisting group chat session located on another chat service.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods are provided for intrusion detection. The systems and methods may include receiving transaction information related to one or more current transactions between a client entity and a resource server, accessing a database storing a plurality of transaction groups, analyzing the received transaction information with respect to information related to at least one of the plurality of transaction groups, and based on said analyzing, determining a possibility of an occurrence of an intrusion act at the resource server. The transaction groups may be formed based on a plurality of past transactions between a plurality of client entities and the resource server. Identity information of a user associated with the one or more current transactions may also be received along with the transaction information. The user may be associated with at least one of the plurality of transaction groups.

Abstract: A system and method is provided for evaluating one or more security policies. Security policies may be analyzed to determine one or more policy attributes based on which one or more policy trees should be generated. These policy trees may be utilized for evaluation purposes.

Abstract: 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.

Abstract: The present invention describes a method for joint modeling of a mean and dispersion of data. A computing system derives a loss function taking into account distributional requirements over the data. The computing system represents separate regression functions for the mean and the dispersion as stagewise expansion forms. At this time, the stagewise expansion forms include undetermined scalar coefficients and undetermined basis functions. Then, the computing system chooses the basis functions that maximally correlate with a corresponding steepest-descent gradient direction of the loss function. The computing system obtains the scalar coefficients based on a single step of Newton iteration. The computing system completes the regression functions based on the chosen basis functions and obtained scalar coefficients.

Abstract: The present invention describes a system and a computer program product for joint modeling of a mean and dispersion of data. A computing system derives a loss function taking into account distributional requirements over the data. The computing system represents separate regression functions for the mean and the dispersion as stagewise expansion forms. At this time, the stagewise expansion forms include undetermined scalar coefficients and undetermined basis functions. Then, the computing system chooses the basis functions that maximally correlate with a corresponding steepest-descent gradient direction of the loss function. The computing system obtains the scalar coefficients based on a single step of Newton iteration. The computing system completes the regression functions based on the chosen basis functions and obtained scalar coefficients.