Oracle International Corporation

Peter J Stengard

Marcos M Campos

Ari Wolfe Mozes

Krisztian Danko

Francisco V Casas

Jooyoung John Kim

Boriana L Milenova

Chitra Balwant Bhagwat

Sunil Venkayala

Burton H Bloom

A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.

Data importer for a sales prospector

Systems and methods provide a system for generating a sales prospect recommendation that uses demographic data to make a sales prospect recommendation that includes a product recommendation with a probability that the sale will close, and may include an estimated time to close the sale and projected revenue. The system imports customer data including past purchasing data and demographic data for a plurality of customers. The system can then generate a predictive model by training the model using the past purchasing data and the demographic data. When queried for a sales prospect recommendation, the system responds to the query with at least one sales prospect recommended by the predictive model.

Hybrid prediction model for a sales prospector

A method, system, and computer program product that automatically generates data mining models of adequate or even optimum quality in a way that reduces the need for user interaction and reduces the cost and improves the quality of model building. A method for automatically building and evaluating data mining models comprises the steps of receiving information specifying models to be built, generating a model setting array based on the information specifying models to be built, the model setting array including at least one model settings combination, preparing specified source data for training and testing each model to be built, for each model settings combination included in the model settings array, performing the steps of building a model based on the model settings combination, evaluating the built model, and outputting results of the model building and evaluation, and selecting a model from the built models based on a selection criterion.

Automated model building and evaluation for data mining system

A data-centric data mining technique provides greater ease of use and flexibility, yet provides high quality data mining results by providing general methodologies for automatic data mining. A methodology for each major type of mining function is provided, including: supervised modeling (classification and regression), feature selection, and ranking, clustering, outlier detection, projection of the data to lower dimensionality, association discovery, and data source comparison. A method for data-centric data mining comprises invoking a data mining feature to perform data mining on a data source, performing data mining on data from the data source using the data mining feature, wherein the data mining feature uses data mining processes and objects internal to the data mining feature and does not use data mining processes and objects external to the data mining feature, outputting data mining results from the data mining feature, and removing all data mining processes and objects internal to the data mining feature that were used to process the data from the data source.

Data-centric automatic data mining

A data mining system for a database management system and method and computer program product therefore, that provides improved functionality over synchronous data mining systems, and which provides features such as interruptible tasks and status output. A data mining system for a database management system comprises a plurality of data mining task objects operable to perform data mining functions, a data mining task queue table operable to maintain at least one queue to manage execution of the data mining tasks, and a data mining system task monitor operable to monitor execution of currently executing data mining tasks, examine the data mining task queue table, select at least one task for execution, dequeue data mining tasks from the data mining task queue table, and initiate execution of the dequeued tasks.

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St. Pete Beach, FL, United States of America

Peter J Stengard