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: 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.

Abstract: 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.

Abstract: 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.

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: 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.

Abstract: The present invention describes a method and system 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: 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.

Abstract: 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.

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 and system 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 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.

Abstract: According to one embodiment, a binary code modification system includes a code modifier configured to access a binary software code. The code modifier generates a modified software code by inserting one or more executable instructions into the binary software code. The one or more executable instructions is operable to expose at least a portion of the binary software code as a web service interface.

Abstract: According to one embodiment, a binary code modification system includes a code modifier configured to access a binary software code. The code modifier generates a modified software code by inserting one or more executable instructions into the binary software code. The one or more executable instructions is operable to provide identity and access management (IAM) functionality or governance functionality to the modified software code.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.