Real-time operating plan data aggregation

• US 7,447,718 B2
• Filed: 04/26/2005
• Issued: 11/04/2008
• 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
• Primary Examiner(s): Vy; Hung T
• Application Number:
  US11/116,143
• Publication Number:
  US 20060080368A1
• Time in Patent Office: 1,288 Days
• Field of Search: 707/101, 707/2, 707/205, 707/100, 707/200, 707/203, 709/223, 709/238, 705/9, 711/153
• 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;

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 forecast data including at least one estimated future value, the OLAP cube to analyze and update the information of the first customer database, and the OLAP cube including a main partition and a change partition, changes of the database being incorporated into the change partition;

new data of the database is entered with a change state flag when such new data is submited, the change state flag indicating a change state of the data wherein a first change state identifies that the data has changed and has been moved from the change partition to the main partition, and a second change state identifies that the data has changed and has not be moved from the change partition to the main partition; and

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.

2. The system of claim 1, 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.

3. The system of claim 2, wherein:

the aggregation of the main partition including incorporating the entries of the change partition is automatically performed periodically at said second predetermined time interval without user intervention to maintain efficiency of collection of a mass amount of data in real time and to maintain system performance; and

the identifying information on the changes being maintained during and after the aggregation, including during the processing of the change partition and during the processing of the main partition, the identifying information including the use identity.

4. The system of claim 2, wherein:

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

the identifying information on the changes further includes the timestamp, and the public window designation associated with the data entered.

5. The system of claim 1, wherein:

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

6. The system of claim 5, wherein:

the username and timestamp information is maintained for the data entered and when processing the entered data to the main partition;

a baseline including a forecast data reference point is generated by marking or logically grouping the relevant changes that make up the baseline; and

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

7. The system of claim 5, wherein:

the public window designation providing a parameter for organizing users and enforcing rules associated with who can and who cannot change data in the system.

8. The system of claim 7, wherein the public window designation providing the parameter so that:

users can examine the data changes that occurred

in the particular public window(s).

9. The system of claim 1, further comprising:

an identification server configured to authenticate a first set of users associated with the first customer and to authenticate a second set of users associated with a second customer;

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

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

the identification server establishing a user identity and a customer identity before data moves into the system, and locating a customer database associated with the customer identity, and linking that customer database to incoming data updates.

10. The system of claim 1, wherein:

the information is financial information.

11. The system of claim 10, wherein the financial information comprises forward looking financial data including a future financial forecast not contained in minds of individual users or any coupled backend forecasting and planning system.

12. The system of claim 1, further cam prising:

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

13. The system of claim 1, wherein:

the change state flag facilitating real-time operation of the system, the data that has the first change state being processed into the change partition in an OLAP cube facilitating low consumption of resources and speedy processing to support the essentially real-time operation of the system as the change data is normally a small part of the total data.

14. A method of maintaining information, comprising:

receiving a set of forecast data, wherein the forecast data includes at least one estimated future value;

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

extracting a baseline forecast from the database, a baseline forecast including a forecast data reference point is generated by marking or logically grouping the relevant changes that make up the baseline;

receiving updates to the database;

propagating updated in a change partition of the OLAP cube, a username and a timestamp information being maintained for the data entered and when processing the entered data to the main partition of the OLAP;

flagging data updated in the database with a change state flag, the change state flag indincating a change state of the data wherein a first change state identifies that the data has moved from the change partition to the main partition, and a second change state identifies that the data has not moved from the change partition to the main partition, the change state flag facilitating real-time operation of the system;

aggregating changes from the change partition into a main partition of the OLAP cube on a first predetermined time interval, the aggregating of the change partition does not include aggregation entries of the change partition into the main partition; and

unflagging the change state flag of data updated in the database.

15. The method of claim 14, 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, a user notification being triggered when a selected value reaches a predetermined threshold.

16. The method of claim 14, 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 14, wherein:

the forecast data is financial information.

21. The method of claim 20, wherein the financial information comprises forward looking financial data including a future financial forecast not contained in minds of individual users or any coupled backend forecasting and planning system.

22. A computer program stored in a computer readable media including executable instructions for performing a method of maintaining information, the method comprising:

receiving a set of forecast data, wherein the forecast data includes at least one estimated future value;

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

extracting a baseline forecast from the database, a baseline forecast including a forecast data reference point is generated by marking or logically grouping the relevant changes that make up the baseline;

receiving updates to the database;

propagating updates in a change partition of the OLAP cube, a username and a timestamp information being maintained for the data entered and when processing the entered data to the main partition of the OLAP;

flagging data updated in the database with a change state flag, the change state flag indicating a change state of the data wherein a first change state identifies that the data has moved from the change partition to the main partition, and a second change state identifies that the data has not moved from the change partition to the main partition, the change state flag facilitating real-time operation of the system;

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

unflagging the change state flag of data updated in the database.