Object-based modeling using composite model object having independently updatable component objects

• US 9,292,592 B2
• Filed: 05/29/2009
• Issued: 03/22/2016
• Est. Priority Date: 05/29/2009
• Status: Active Grant

• Current Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Original Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Inventors: Williamson, Eric
• Primary Examiner(s): PEACH, POLINA G
• Application Number:
  US12/475,452
• Publication Number:
  US 20100306254A1
• Time in Patent Office: 2,489 Days
• Field of Search: 715/200, 715/249, 715/804
• US Class Current: 1/1
• CPC Class Codes: G06F 16/289 Object oriented databases
• CPC Class Codes: G06F 9/543 User-generated data transfer, e.g. clipboards, dynamic data exchange [DDE], object linking and embedding [OLE]

Abstract

Embodiments relate to systems and methods for object-based modeling using a composite model object having independently updatable component objects. 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 spreadsheet, database or other content can be extracted and encapsulated in extensible markup language (XML) or other object-oriented format. Various model objects can be extracted from source data, and aggregated into a composite model object encapsulating the underlying set of component model objects. In aspects, each of the component model objects can be accessed, updated, and stored independently of the other constituent objects. The modeling client can exchange composite or other modeling objects with external platforms on a programmatic basis.

Claims

1. A method comprising;

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

extracting a first set of data elements and a first set of functional relationships from a first column of cells of the set of source data, and a second set of data elements and a second set of functional relationships from a second column of cells of the set of source data;

encapsulating the first set of data elements and the first set of functional relationships in a first component predictive model object, and the second set of data elements and the second set of functional relationships in a second component predictive model object;

encoding, by a processor, a linkage in the second component predictive model object linking the second component predictive model object with the first component predictive model object;

encoding a linkage in the first component predictive model object linking the first component predictive model object with the second component predictive model object, wherein the linkage in the first component predictive model object and the linkage in the second component predictive model object creates a composite predictive model object;

simultaneously operating a plurality of modeling tools on the first component predictive model object and the second component predictive model object of the composite predictive model object;

updating the first component predictive model object in the composite predictive model object via the simultaneously operating plurality of modeling tools without overwriting the second component predictive model object that is linked to the first component predictive model; and

updating the second component predictive model object in the composite predictive model object via the simultaneously operating plurality of modeling tools without overwriting the first component predictive model object that is linked to the second component predictive model.

2. The method of claim 1, wherein the first component predictive model object and the second component predictive model object are selectively viewable.

3. The method of claim 1, wherein the first component predictive model object and the second component predictive model object are encoded in extensible markup language (XML) format.

4. The method of claim 1, further comprising storing the second component predictive model object in at least one of local storage or remote storage.

5. The method of claim 1, wherein the set of source data comprises at least one attribute, the at least one attribute comprising at least one of a function call parameter or a linkage parameter.

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

7. The method of claim 1, wherein the set of source data is hosted in at least one of local storage or remote storage.

8. A system comprising:

an interface to a set of source data; and

a processor, to communicate with the set of source data via the interface, the processor to;

invoke a multi-dimensional relational database application, the multi-dimensional relational database application comprising the set of source data,

extract a first set of data elements and a first set of functional relationships from a first column of cells of the set of source data, and a second set of data elements and a second set of functional relationships from a second column of cells of the set of source data,

encapsulate the first set of data elements and the first set of functional relationships in a first component predictive model object, and the second set of data elements and the second set of functional relationships in a second component predictive model object,

encode a linkage in the second component predictive model object linking the second component predictive model object with the first component predictive model;

encode a linkage in the first component predictive model object linking the first component predictive model object with the second component predictive model object, wherein the linkage in the first component predictive model object and the linkage in the second component predictive model object creates a composite predictive model object;

simultaneously operate a plurality of modeling tools on the first component predictive model object and the second component predictive model object of the composite predictive model object;

update the first component predictive model object in the composite predictive model object via the simultaneously operating plurality of modeling tools without overwriting the second component predictive model object that is linked to the first component predictive model; and

update the second component predictive model object in the composite predictive model object via the simultaneously operating plurality of modeling tools without overwriting the first component predictive model object that is linked to the second component predictive model.

9. The system of claim 8, wherein the first component predictive model object and the second component predictive model object are selectively viewable.

10. The system of claim 8, wherein the first component predictive model object and the second component predictive model object are encoded in extensible markup language (XML) format.

11. The system of claim 8, further comprising at least one of:

a data store coupled to the processor to store the second component predictive model object or remote storage coupled to the processor to store the second component predictive model object.

12. The system of claim 8, wherein the set of source data comprises at least one attribute, the at least one attribute comprising at least one of a function call parameter or a linkage parameter.

13. The system of claim 8, wherein the second component predictive model object is exportable via an application programming interface (API).

14. The system of claim 8, where the set of source data is hosted in at least one of local storage or remote storage.

15. A non-transitory computer readable storage media including instructions, that when executed by a processor, cause the processor to:

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

extract a first set of data elements and a first set of functional relationships from a first column of cells of the set of source data, and a second set of data elements and a second set of functional relationships from a second column of cells of the set of source data;

encapsulate the first set of data elements and the first set of functional relationships in a first component predictive model object, and the second set of data elements and the second set of functional relationships in a second component predictive model object;

encode, by the processor, a linkage in the second component predictive model object linking the second component predictive model object with the first component predictive model object;

encode a linkage in the first component predictive model object linking the first component predictive model object with the second component predictive model object, wherein the linkage in the first component predictive model object and the linkage in the second component predictive model object creates a composite predictive model object;

simultaneously operate a plurality of modeling tools on the first component predictive model object and the second component predictive model object of the composite predictive model object;

update the first component predictive model object in the composite predictive model object via the simultaneously operating plurality of modeling tools without overwriting the second component predictive model object that is linked to the first component predictive model; and

update the second component predictive model object in the composite predictive model object via the simultaneously operating plurality of modeling tools without overwriting the first component predictive model object that is linked to the second component predictive model.

16. The computer readable storage media of claim 15, wherein the second component predictive model object is exportable via an application programming interface (API).