Attribute segments and data table bias reduction

• US 7,949,639 B2
• Filed: 01/29/2008
• Issued: 05/24/2011
• Est. Priority Date: 02/20/2004
• Status: Active Grant

• Current Assignee: Information Resources Incorporated
• Original Assignee: Symphonyiri Group Incorporated (New Mountain Capital Group LLC)
• Inventors: Hudson, Gregory David Neil, Basilico, Andrea, Gibbs, Marshall Ashby, Chapa, Craig Joseph, Yusko, Jay Alan, Griglione, Bradley Michael, Mason, Trevor, Hunt, Herbert Dennis, Agostinelli, Alberto, West, John Randall, Johnson, Arvid C., Bergeon, Cheryl G.
• Primary Examiner(s): Lovel; Kimberly
• Assistant Examiner(s): Uddin; Mohammed R
• Application Number:
  US12/021,495
• Publication Number:
  US 20090006490A1
• Time in Patent Office: 1,211 Days
• Field of Search: 707/688, 707/776, 707/803, 707/999.001, 707/705
• US Class Current: 707/688
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

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.

Claims

1. A non-transitory computer readable medium bearing instructions that, when executed, cause one or more computers to:

identify;

retail data associated with a first entity, the retail data including data from a first data source and a second data source, the retail data further including product identifiers;

retail data associated with a second entity, the retail data including product identifiers, the retail data including data from a third data source; and

a plurality of factor calculations;

retrieve, based on the product identifiers, a plurality of overlapping attribute segments to use for comparing the data from the first and second data sources;

compare the plurality of the overlapping attribute segments;

calculate a plurality of factors for each of the overlapping attribute segments, each factor representing a bias present in the second data source, and

use the factors to adjust the values in the retail data from the third data source, thereby reduce bias present in the third source.

2. The medium of claim 1, wherein the instructions further cause the one or more computers to use the factors to update the retail data from the third data source to reduce incompleteness.

3. The medium of claim 2, wherein the instructions further cause the one or more computers to calculate a blended factor when at least two measures are available for a same factor, said blended factor being used to reduce bias in the second or third data source.

4. The medium of claim 2, wherein the instructions further cause the one or more computers to calculate a blended factor when at least two measures are available for a same factor, said blended factor being used to reduce bias in the second data source.

5. The medium of claim 1, wherein the instructions further cause the one or more computers to calculate the factor for an overlapping attribute segment by dividing a first data source volume amount by a corresponding second data source volume amount.

6. The medium of claim 1, wherein the instructions further cause the one or more computers to apply the factor for the attribute segment to the retail data from the third data source by multiplying a plurality of third data source volume amounts by the factor for the corresponding attribute segment.

7. The medium of claim 6, wherein the instructions further cause the one or more computers to calculate the blended factor by giving a more accurate data source a higher relative weight and by giving a less accurate data source a lower relative weight.

8. The medium of claim 1, wherein the instructions further cause the one or more computers to only calculate the factors for each attribute that is identified in the server to be relevant.

9. The medium of claim 1, wherein the instructions further cause the one or more computers to only calculate the factors for attribute segments that are determined to be significant.

10. The medium of claim 9, wherein the instructions further cause the one or more computers to calculate the blended factor by giving the more accurate data source a higher relative weight and by giving the less accurate data source a lower relative weight.

11. The medium of claim 1, wherein the instructions further cause the one or more computers to save each factor in a database.