Real-time forecasting

• US 20060080368A1
• Filed: 04/26/2005
• Published: 04/13/2006
• Est. Priority Date: 04/26/2004
• Status: Active Grant

• Current Assignee: Ignite Enterprise Software Solutions, Inc. (Infonow Corporation)
• Original Assignee: Right90, Inc. (ESW Capital, LLC)
• Inventors: Skelton, Dean, Petiot, David, Orumchian, Kim, Stabenow, Art
• Granted Patent: US 7,447,718 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 2216/03 Data mining
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99956 File allocation

Abstract

In one embodiment, a system is provided. The system includes a first client. The system also includes an analysis server coupled to the first client. The system further includes a first customer database of information coupled to the analysis server. The first customer database is to embody forecast data and to receive essentially real-time updates to the forecast data. The first customer database supports an OLAP cube associated with the analysis server.

Claims

1. A system comprising:

a first client;

an analysis server incorporating an OLAP cube coupled to the first client; and

a first customer database of information coupled to the analysis server, the first customer database is to embody forecast data and to receive essentially real-time updates to the forecast data, the OLAP cube to analyze and update the information of the first customer database.

2. The system of claim 1, wherein:

the OLAP cube includes a main partition and a change partition, changes of the database incorporated into the change partition.

3. The system of claim 2, wherein:

new data of the database is entered with a change flag when such new data is submitted.

4. The system of claim 3, wherein:

The change partition is aggregated by the OLAP cube on a first predetermined time interval, the aggregating of the change partition does not include aggregating entries of the change partition into the main partition.

5. The system of claim 4, wherein:

The main partition is aggregated by the OLAP cube on a second predetermined time interval, the second predetermined time interval greater than the first predetermined time interval, the aggregation of the main partition includes incorporating the entries of the change partition.

6. The system of claim 1, wherein:

data is entered in the database with a timestamp and a public window designation associated therewith.

7. The system of claim 6, wherein:

baselines are maintained within the database, the baselines including data based on the window value associated with the data and window values designated for the baseline.

8. The system of claim 1, further comprising:

A second client coupled to the identification server and the analysis server; and

A second customer database of information coupled to the identification server and the analysis server, the second customer database dedicated to use by a second set of users of the system.

9. The system of claim 1, wherein:

The information is financial information.

10. The system of claim 1, further comprising:

means for providing data to the first client from a location remote from a location of the first client.

11. A method of maintaining information, comprising:

receiving a set of forecast data;

incorporating the forecast data into a database of the information through an OLAP cube;

extracting a baseline forecast from the database;

receiving updates to the database; and

propagating updates in a change partition of the OLAP cube.

12. The method of claim 11, further comprising:

flagging data updated in the database with a change flag.

13. The method of claim 12, further comprising:

aggregating changes from the change partition into a main partition of the OLAP cube; and

unflagging the change flag of data updated in the database.

14. The method of claim 11, further comprising:

watching a set of watched data points of the information; and

notifying a user of changes in the set of watched data points responsive to the propagating and receiving updates.

15. The method of claim 11, wherein:

the method is executed by a processor in response to instructions, the instructions embodied in a machine-readable medium.

16. The method of claim 11, further comprising:

designating a window value when updates to the database are received, the window value associated with the updates;

recording a timestamp associated with updates to the database; and

deriving a baseline from values of data in the database, the data selected according to a window value designated for the baseline.

17. The method of claim 16, wherein:

the baseline is derived from a previous baseline and updates received in the database since derivation of the previous baseline.

18. The method of claim 16, wherein:

the baseline is derived responsive to a user request for a private baseline incorporating a subset of the data of the database, the subset determined based on window value associated with the data.

19. The method of claim 16, wherein:

the baseline is derived from a subset of a set of previous baselines and updates received in the database since derivation of the previous baseline having window values of a selected subset of available window values.

20. The method of claim 11, wherein:

The forecast data is financial information.