Dimensional compression using an analytic platform

• US 9,390,158 B2
• Filed: 01/31/2008
• Issued: 07/12/2016
• Est. Priority Date: 01/26/2007
• Status: Active Grant

• Current Assignee: Information Resources Incorporated
• Original Assignee: Information Resources Incorporated
• Inventors: Hunt, Herbert Dennis, West, John Randall, Gibbs, Marshall Ashby Jr., Griglione, Bradley Michael, Hudson, Gregory David Neil, Basilico, Andrea, Johnson, Arvid Conrad, Bergeon, Cheryl G., Chapa, Craig Joseph, Agostinelli, Alberto, Yusko, Jay Alan, Mason, Trevor
• Primary Examiner(s): AL HASHEMI, SANA A
• Application Number:
  US12/023,283
• Publication Number:
  US 20080288538A1
• Time in Patent Office: 3,085 Days
• Field of Search: 707/607, 707/687, 707/705, 707/790, 707/813, 707/821
• US Class Current: 1/1
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Abstract

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.

Claims

1. A method comprising:

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;

prior to receiving a query, pre-aggregating a plurality of combinations of a plurality of causal fact data and associated data attributes in a causal bitmap;

selecting a subset of the pre-aggregated combinations based on suitability of a combination for an analytic purpose; and

storing the subset of pre-aggregated combinations to facilitate querying of the subset.

2. The method of claim 1 wherein the data attribute is a product attribute.

3. The method of claim 1 wherein the product attribute is a nutritional level.

4. The method of claim 1 wherein the product attribute is a brand.

5. The method of claim 1 wherein the product attribute is product category.

6. The method of claim 1 wherein the product attribute is based at least in part on a SKU.

7. The method of claim 1 wherein the product attribute is physical attribute.

8. The method of claim 1 wherein the causal fact datum is a consumer attribute.

9. The method of claim 1, further comprising prior to receiving the query, pre-aggregating point of sale data with the plurality of combinations of the plurality of causal fact data and associated data attributes.

10. The method of claim 8 wherein the consumer attribute is a consumer geography.

11. The method of claim 8 wherein the consumer attribute is a consumer category.

12. The method of claim 8 wherein the consumer attribute is a consumer demographic.

13. The method of claim 8 wherein the consumer attribute is a consumer behavior.

14. The method of claim 8 wherein the consumer attribute is a consumer life stage.

15. The method of claim 8 wherein the consumer attribute is a retailer-specific customer attribute.

16. The method of claim 8 wherein the consumer attribute is an ethnicity.

17. The method of claim 8 wherein the consumer attribute is an income level.

18. The method of claim 8 wherein the consumer attribute is media usage type.

19. The method of claim 8 wherein the consumer attribute is a media usage level.

20. The method of claim 8 wherein the consumer attribute is a technology usage type.

21. The method of claim 8 wherein the consumer attribute is a user-created custom consumer attribute.