Systems and methods for extracting database dimensions as data modeling object

• US 8,606,827 B2
• Filed: 05/29/2009
• Issued: 12/10/2013
• Est. Priority Date: 05/29/2009
• Status: Expired due to Fees

• Current Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Original Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Inventors: Williamson, Eric
• Primary Examiner(s): Casanova, Jorge A
• Application Number:
  US12/475,441
• Publication Number:
  US 20100306281A1
• Time in Patent Office: 1,656 Days
• Field of Search: 707/796, 707/803, 707/804, 707/810
• US Class Current: 707/804
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

Embodiments relate to systems and methods for extracting a model object from a multi-dimensional source database. A modeling client can host modeling logic and an application programming interface (API) to create, access, manipulate, and import/export modeling objects used in modeling applications, such as engineering, medical, financial, and other modeling platforms. In aspects, the source data accepted into the modeling client can include consumer or business-level applications, whose database or other content can be extracted and encapsulated in object-oriented format, such as extensible markup language (XML) format. The resulting model object can be pivoted along selected dimensions, or otherwise manipulated. The modeling client can exchange one or more modeling object directly with external platforms, such as mainframe modeling platforms, via the application programming interface (API) on a programmatic basis. Costs and maintenance savings over mainframe-based modeling tools can thereby be achieved, while providing greater power than consumer or business-level tools.

Claims

1. A method comprising:

invoking a multi-dimensional relational database, the multi-dimensional relational database comprising a set of source data;

selecting a portion of the multi-dimensional relational database to extract a set of data elements and a set of functional relationships describing relationships between values of the set of data elements of the selected portion;

encapsulating the set of data elements and the set of functional relationships, of the selected portion, in a predictive database model object, wherein a modeling application performs an operation on the predictive database model object to predict an output;

sending, by a client computing system, the predictive database model object, comprising the portion of the multi-dimensional relational database, to a mainframe platform, wherein the mainframe platform performs a modeling operation using the set of data elements and the set of functional relationships in the predictive database model object to change at least one of the set of data elements or the set of functional relationships in the predictive database model object; and

receiving a modified predictive database model object from the mainframe platform, wherein the modified predictive database model object comprises at least one of a changed set of data elements or a changed set of functional relationships.

2. The method of claim 1, wherein the predictive database model object and the modified predictive database model object are pivotable on a selected dimension of the multi-dimensional relational database.

3. The method of claim 2, wherein the selected dimension comprises one of a row, a column, a plane, or a cell.

4. The method of claim 1, wherein the set of data elements comprises a set of database values.

5. The method of claim 1, wherein the predictive database model object is encoded in extensible markup language (XML) format.

6. The method of claim 1, wherein the set of data elements comprises at least one of a function call parameter or a linkage parameter.

7. The method of claim 1, wherein the predictive database model object is extensible via an appending of an additional extracted set of data elements to the predictive database model object.

8. The method of claim 1, wherein the predictive database model object is exportable via an application programming interface (API).

9. The method of claim 1, further comprising:

converting the modified predictive database model object received from the mainframe platform to a non-object oriented format.

10. A client system, comprising:

an interface to a set of source data, the set of source data hosted in a multi-dimensional relational database; and

a processor coupled to the interface to;

invoke the multi-dimensional relational database,

select a portion of the multi-dimensional relational database to extract a set of data elements and a set of functional relationships describing relationships between values of the set of data elements of the selected portion,

encapsulate the set of data elements and the set of functional relationships, of the selected portion, in a predictive database model object, wherein a modeling application performs an operation on the predictive database model object to predict an output;

send the predictive database model object, comprising the portion of the multi-dimensional relational database, to a mainframe platform, wherein the mainframe platform performs a modeling operation using the set of data elements and the set of functional relationships in the predictive database model object to change at least one of the set of data elements or the set of functional relationships in the predictive database model object; and

receive a modified predictive database model object from the mainframe platform, wherein the modified predictive database model object comprises at least one of a changed set of data elements or a changed set of functional relationships.

11. The system of claim 10, wherein the predictive database model object and the modified predictive database model object are pivotable on a selected dimension of the multi-dimensional relational database.

12. The system of claim 11, wherein the selected dimension comprises one of at least a row, a column, a plane, or a cell.

13. The system of claim 11, wherein the set of data elements comprises at least one of a function call parameter or a linkage parameter.

14. The system of claim 10, wherein the set of data elements comprises a set of database values.

15. The system of claim 10, wherein the predictive database model object is encoded in extensible markup language (XML) format.

16. The system of claim 10, wherein the predictive database model object is extensible via an appending of an additional extracted set of data elements to the predictive database model object.

17. The system of claim 10, wherein the predictive database model object is exportable via an application programming interface (API).

18. The system of claim 10, wherein the processor is further to:

convert the modified predictive database model object received from the mainframe platform to a non-object oriented format.

19. A non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations comprising:

invoking a multi-dimensional relational database, the multi-dimensional relational database comprising a set of source data;

selecting a portion of the multi-dimensional relational database to extract a set of data elements and a set of functional relationships describing relationships between values of the set of data elements of the selected portion;

encapsulating the set of data elements and the set of functional relationships, of the selected portion, in a predictive database model object, wherein a modeling application performs an operation on the predictive database model object to predict an output;

sending, by the processor, the predictive database model object, comprising the portion of the multi-dimensional relational database, to a mainframe platform, wherein the mainframe platform performs a modeling operation using the set of data elements and the set of functional relationships in the predictive database model object to change at least one of the set of data elements or the set of functional relationships in the predictive database model object; and

receiving a modified predictive database model object from the mainframe platform, wherein the modified predictive database model object comprises at least one of a changed set of data elements or a changed set of functional relationships.

20. The non-transitory computer readable storage medium of claim 19, the operations further comprising:

converting the modified predictive database model object received from the mainframe platform to a non-object oriented format.