Cross-category view of a dataset using an analytic platform

• US 10,621,203 B2
• Filed: 01/28/2008
• Issued: 04/14/2020
• 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, Jr., Marshall Ashby, 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): Trujillo, James
• Assistant Examiner(s): Morris, John J
• Application Number:
  US12/021,268
• Publication Number:
  US 20090018996A1
• Time in Patent Office: 4,460 Days
• Field of Search: 707600, 707603, 707736, 707752, 707776
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

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.

Claims

1. A method for facilitating high-speed user exploration of a dimension of projected sales information using actual sales data and projection data in a data warehouse, the method comprising:

receiving a data set in an analytic platform, the data set including sales data and projection weights applied to the sales data to calculate projections of future sales data;

creating a flexible dimension for a query of aggregated data by, prior to receiving the query, pre-aggregating the sales data and projection weights along a primary sorting dimension into a projected fact table containing projected sales resulting from aggregating the sales data and the projection weights, the projected sales grouped by one or more of time, item, and venue and stored keyed by one or more corresponding keys of time, item, and venue, where an aggregating query in the flexible dimension is used as a lookup in the projected fact table for any values of the flexible dimension used for the pre-aggregating and represented in an index containing references to corresponding facts in the projected fact table;

sorting projected facts in the projected fact table by the primary sorting dimension; and

processing the query against the sales data with respect to the primary sorting dimension using pre-aggregated data in the projected fact table when the query does not seek to vary the primary sorting dimension for the projected fact table and executing a new query against the sales data when the query seeks to vary the primary sorting dimension for the fact table to establish flexibility with respect to at least one dimension for performing computations when the query seeks to vary the primary sorting dimension, wherein when the query does not seek to vary the primary sorting dimension and requires a number of facts in the pre-aggregated data, processing the query includes locating the number of facts and summing a corresponding number of values for the number of facts to provide an analytic result to the query.

2. The method of claim 1, further comprising, prior to receiving the query, sorting the projected facts by a secondary sorting dimension.

3. The method of claim 2, further comprising, prior to receiving the query, sorting the projected facts by a tertiary sorting dimension.

4. The method of claim 3, wherein the primary, secondary, and tertiary sorting dimensions are selected from the group consisting of venue, time, and item.

5. The method of claim 1 wherein the projected sales are aggregated by time and stored keyed by time.

6. The method of claim 1 wherein the projected sales are aggregated by venue and stored keyed by venue.

7. The method of claim 1 wherein the projected sales are aggregated by item and stored keyed by item.

8. The method of claim 1 wherein the projections of future sales data include projections for a plurality of distinct product categories.

9. The method of claim 1 wherein the primary sorting dimension is time.

10. The method of claim 1 wherein processing the query with respect to the primary sorting dimension further comprises interactively serving results of the query back to a user from the projected fact table in response to a user input via an interface that permits the user to interactively select a point along the primary sorting dimension and view projected results of the query corresponding to the selected point, without causing the analytic platform to reprocess the query during the interactive point selection.

11. The method of claim 1 further comprising processing a second query against the sales data with respect to a dimension other than the primary sorting dimension by generating another aggregation from the data set.

12. A computer program product for facilitating high-speed user exploration of a dimension of projected sales information using actual sales data and projection data in a data warehouse, the computer program product comprising computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of:

receiving a data set in an analytic platform, the data set including sales data and projection weights applied to the sales data to calculate projections of future sales data;

creating a flexible dimension for a query of aggregated data by, prior to receiving the query, pre-aggregating the sales data and projection weights along a primary sorting dimension into a projected fact table containing projected sales resulting from aggregating the sales data and the projection weights, the projected sales grouped by one or more of time, item, and venue and stored keyed by one or more corresponding keys of time, item, and venue, where an aggregating query in the flexible dimension is used as a lookup in the projected fact table for any values of the flexible dimension used for the pre-aggregating and represented in an index containing references to corresponding facts in the projected fact table;

sorting projected facts in the projected fact table by the primary sorting dimension; and

processing the query against the sales data with respect to the primary sorting dimension using pre-aggregated data in the projected fact table when the query does not seek to vary the primary sorting dimension for the projected fact table and executing a new query against the sales data when the query seeks to vary the primary sorting dimension for the fact table to establish flexibility with respect to at least one dimension for performing computations when the query seeks to vary the primary sorting dimension, wherein when the query does not seek to vary the primary sorting dimension and requires a number of facts in the pre-aggregated data, processing the query includes locating the number of facts and summing a corresponding number of values for the number of facts to provide an analytic result to the query.

13. The computer program product of claim 12, further comprising code that performs the step of, prior to receiving the query, sorting the projected facts by a secondary sorting dimension.

14. The computer program product of claim 13, further comprising code that performs the step of, prior to receiving the query, sorting the projected facts by a tertiary sorting dimension.

15. The computer program product of claim 14, wherein the primary, secondary, and tertiary sorting dimensions are selected from the group consisting of venue, time, and item.

16. The computer program product of claim 12 wherein the projected sales are aggregated by time and stored keyed by time.

17. The computer program product of claim 12 wherein the projected sales are aggregated by venue and stored keyed by venue.

18. The computer program product of claim 12 wherein the primary sorting dimension is time.

19. The computer program product of claim 12 wherein processing the query with respect to the primary sorting dimension further comprises interactively serving results of the query back to a user from the projected fact table in response to a user input via an interface that permits the user to interactively select a point along the primary sorting dimension and view projected results of the query corresponding to the selected point, without causing the analytic platform to reprocess the query during the interactive point selection.

20. The computer program product of claim 12 further comprising code that performs the step of processing a second query against the sales data with respect to a dimension other than the primary sorting dimension by generating another aggregation from the data set.