Teradata Us, Inc.

Ncr Corporation

Other

Paul M Cereghini

Karen A Papierniak

Thomas Kevin Ryan

Carlos Ordonez

Sreedhar Srikant

Brian Jay Wasserman

George Robert Hood

Linette Draper

Mike Chiufang Hsiao

Eric D Dunsker

Scott Woodroofe Cunningham

Gordon Brooks

Kavitha Devarakonda

Gordon Douglas Brooks, Jr

Giai Do

Ahsan U Haque

Judy Bayer

Ellen Boerger

Enrico Galimberti

Eric D Dusker

A data warehouse system and application which analyzes historical sales and product data contained within a data warehouse to determine the best product prices across a set of products for a retailer. The application analyzes historical sales and product data contained in a database to determine an opportunity score for multiple products sold by a retailer, the opportunity score indicating potential benefit to said business from changing the pricing of a product; and analyzes historical sales data contained in the database to determine an ability to change score for each product, the ability to change score indicating potential risk of lost sales for the retailer from changing the pricing of a product. Results of the analyses are displayed in a scatter plot graph with the graph axes being the opportunity scores and ability to change scores, respectively. Each product is represented by a point in the scatter plot, the coordinates of the point being the opportunity score and the ability to change score associated with the represented product. The scatter plot graph can be divided into quadrants, wherein products having favorable opportunity scores and favorable ability to change scores are displayed together in one the four quadrants.

Opportunity matrix for use with methods and systems for determining optimal pricing of retail products

Techniques are presented for managing fraud information. Metadata defines user profiles, security levels, fraud cases, and presentation information. One or more queries or reports are processed against disparate data store tables and the results are aggregated into a repository. The repository is also defined by the metadata. Furthermore, operations associated with sharing, viewing, and accessing the results from the repository is defined and controlled by the metadata. In an embodiment, portions of the metadata may be viewed and navigated in a hierarchical and graphical formatted presentation.

Techniques for managing fraud information

A method of graphically displaying the path of a customer traversing a web site and related business data is described. The method includes receiving a user request for a visualization. The user request may include data filters and exclusions. Responsive to the user request, traffic data is selected for analysis. The selected traffic data is analyzed and displayed to the user. The display may be in the form of a visualization including a graph and related business data. The graph may be of an overview, referral, path, page-to-page path, and animation type. A system for visualizing traffic patterns and the path of a customer at a site is described in conjunction with the above method. The system includes a logical data model, a dimensional data model, a report specification, a graphical interface, metadata database and an analysis report. The graphical interface is used for viewing visualization information.

Integration of visualizations, reports, and data

Methods and systems for locating related reports are provided. A report application identifies a first report to process along with preference selections associated with finding related reports to the first report being processed. A data preparation application performs an initial filter against a report data store using the first report and the preference selections. The data preparation application passes candidate reports and the first report to a data mining application. The data mining application uses the first report, the candidate reports, and a report usage data store to find relationships and associations with additional reports. In one embodiment, the additional reports are ranked and organized before being presented to the report application as related reports to the first report.

Methods and systems for locating related reports

Report specification generators, interfaces, and methods are provided. Existing specification objects are selected from a data store. New specification objects are created as shell specification objects. Moreover, computations are selected from the data store. The specification objects, the shell specification objects, and the computations are organized within a grid. In one embodiment, the grid is used to generate a template report schema. In another embodiment, a preview tool populates the grid with report data in order to present a preview.

Report specification generators and interfaces

A computer-implemented data mining system includes an Interface Tier, an Analysis Tier, and a Database Tier. The Interface Tier supports interaction with users, and includes an On-Line Analytic Processing (OLAP) Client that provides a user interface for generating SQL statements that retrieve data from a database, and an Analysis Client that displays results from a data mining algorithm. The Analysis Tier performs one or more data mining algorithms, and includes an OLAP Server that schedules and prioritizes the SQL statements received from the OLAP Client, an Analytic Server that schedules and invokes the data mining algorithm to analyze the data retrieved from the database, and a Learning Engine performs a Learning step of the data mining algorithm. The Database Tier stores and manages the databases, and includes an Inference Engine that performs an Inference step of the data mining algorithm, a relational database management system (RDBMS) that performs the SQL statements against a Data Mining View to retrieve the data from the database, and a Model Results Table that stores the results of the data mining algorithm.

Architecture for distributed relational data mining systems

A method for performing cluster analysis inside a relational database management system. The method defines a plurality of tables for the storage of data points and Gaussian mixture parameters and executes a series of SQL statements implementing an Expectation-Maximization clustering algorithm to iteratively update the Gaussian mixture parameters stored within the tables.

Horizontal implementation of expectation-maximization algorithm in SQL for performing clustering in very large databases

Vertical implementation of expectation-maximization algorithm in SQL for performing clustering in very large databases

Method for performing clustering in very large databases

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San Diego, CA, United States of America

Paul M Cereghini