Information Resources, Inc.

Symphonyiri Group, Inc.

Cheryl G Bergeon

Arvid Conrad Johnson

Herbert Dennis Hunt

John Randall West

Jay Alan Yusko

Bradley Michael Griglione

Craig Joseph Chapa

Alberto Agostinelli

Gregory David Neil Hudson

Trevor Mason

Andrea Basilico

Marshall Ashby Gibbs, Jr

Michael W Kruger

Ting Liu

In embodiments, systems and methods may involve using a platform as disclosed herein for applications described herein where the systems and methods involve receiving a dataset in an analytic platform, the dataset including fact data and dimension data for a plurality of distinct product categories. It may also involve storing the data in a flexible hierarchy, the hierarchy allowing the temporary fixing of data along a dimension and flexible querying along other dimensions of the data. It may also involve pre-aggregating certain combinations of data to facilitate rapid querying, the pre-aggregation based on the nature of common queries. It may also involve facilitating the presentation of a cross-category view of an analytic query of the dataset. In embodiments, the temporarily fixed dimension can be rendered flexible upon an action by the user.

Cross-category view of a dataset using an analytic platform

In embodiments of the present invention, a method is described for reducing bias by data fusion of a household panel data and a loyalty card data. In embodiments, a method is provided for receiving a consumer panel dataset in a data fusion facility, receiving a consumer point-of-sale dataset in a data fusion facility, receiving a dimension dataset in a data fusion facility, fusing the datasets received in the data fusion facility into a new panel dataset based at least in part on an encryption key, estimating a consumer behavior using a first model based on the consumer panel dataset, estimating a consumer behavior using a second model based only on those consumers present in both the consumer panel dataset and the consumer point-of-sale dataset, and refining the first model based at least on the results of the second model.

Bias reduction using data fusion of household panel data and transaction data

Using a computer, a database comprising a field is identified. A query relating to the field is identified. Prior to processing the query, the field is dynamically altered to conform to a desired bit size. The query is processed. The results of the query are returned.

Cluster processing of an aggregated dataset

Systems and methods are presented that may involve receiving a causal fact dataset including facts relating to items perceived to cause actions, wherein the causal fact dataset includes a data attribute that is associated with a causal fact datum. It may also involve pre-aggregating a plurality of the combinations of a plurality of causal fact data and associated data attributes in a causal bitmap. It may also involve selecting a subset of the pre-aggregated combinations based on suitability of a combination for the analytic purpose. It may also involve storing the subset of pre-aggregated combinations to facilitate querying of the subset.

Dimensional compression using an analytic platform

In embodiments of the present invention improved capabilities are described for identifying a first classification scheme associated with product attributes of a first grouping of products, identifying a second classification scheme associated with product attributes of a second grouping of products, and receiving a record of data relating to an item, the classification of which is uncertain. It may also involve receiving a dictionary of attributes associated with products and assigning the item to at least one of the classification schemes based on probabilistic matching among the attributes in the classification schemes, the attributes in the dictionary of attributes and the known attributes of the item.

Similarity matching of products based on multiple classification schemes

In embodiments of the present invention improved capabilities are described for identifying a classification scheme associated with product attributes of a grouping of products of an entity, receiving a record of data relating to an item of a competitor to the entity, the classification of which is uncertain, receiving a dictionary of attributes associated with products, and assigning a product code to the item, based on probabilistic matching among the attributes in the classification scheme, the attributes in the dictionary of attributes and at least one known attribute of the item.

Similarity matching of a competitor's products

The present invention provides a method for updating data sources. The method may include identifying a plurality of data sources, identifying a plurality of overlapping attribute segments to use for comparing the data sources, calculating a factor as a function of each of the plurality of overlapping attribute segments, and using the factors to update a first group of values in the second data source to reduce bias. Further, at least a first data source is more accurate than a second data source.

Attribute segments and data table bias reduction

A computer system and method is disclosed that analyzes and corrects retail data. The system and method includes several client workstations and one or more servers coupled together over a network. A database stores various data used by the system. A business logic server uses competitive and complementary fusion to analyze and correct some of the data sources stored in database server. The data fusion process itself is an iterative one&#x2014;utilizing both competitive and complementary fusion methods. In competitive fusion, two or more data sources that provide overlapping attributes are compared against each other. More accurate/reliable sources are used to correct less accurate/reliable sources. In complementary fusion, relationships modeled where data sources overlap are projected to areas of the data framework in which fewer sources exist&#x2014;enhancing the accuracy/reliability of those fewer sources even in the absence of the other sources upon which the models were based.

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Arlington Heights, IL, United States of America

Cheryl G Bergeon