Dimensionally constrained synthetic context objects database

• US 9,069,838 B2
• Filed: 11/12/2013
• Issued: 06/30/2015
• Est. Priority Date: 09/11/2012
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Adams, Samuel S., Friedlander, Robert R., Kraemer, James R.
• Primary Examiner(s): Mahmoudi, Tony
• Assistant Examiner(s): NGUYEN, MERILYN P
• Application Number:
  US14/078,135
• Publication Number:
  US 20140074892A1
• Time in Patent Office: 595 Days
• Field of Search: 707/792, 707/794, 707/802, 707/797
• US Class Current: 1/1
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/282 Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes
• CPC Class Codes: G06F 16/289 Object oriented databases
• CPC Class Codes: G06F 2216/03 Data mining
• CPC Class Codes: G06F 7/00 Methods or arrangements for processing data by operating upon the order or content of the data handled logic circuits H03K19/00

Abstract

A processor-implemented method, system, and/or computer program product generate and utilize a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects. A non-contextual data object is associated with a context object to define a synthetic context-based object, where the non-contextual data object ambiguously relates to multiple subject-matters, and where the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object. The synthetic context-based object is then associated with at least one specific data store, which includes data that is associated with data contained in the non-contextual data object and the context object. A dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects is then constructed for handling requests for data stores.

Claims

1. A processor-implemented method for generating and utilizing a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, the processor-implemented method comprising:

associating, by a processor, a non-contextual data object with a context object to define a synthetic context-based object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object;

associating, by the processor, the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is associated with data contained in the non-contextual data object and the context object; and

constructing, by the processor, a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same non-contextual data object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different context objects.

2. The processor-implemented method of claim 1, further comprising:

data mining, by the processor, a data structure for the non-contextual data object and the context object, wherein said data mining locates said at least one specific data store that comprises data contained in the non-contextual data object and the context object.

3. The processor-implemented method of claim 1, wherein the specific subject-matter for a particular data store in the data structure is exclusive to only said particular data store.

4. The processor-implemented method of claim 1, wherein the specific subject-matter for a particular data store in the data structure overlaps a subject-matter of another data store in the data structure.

5. The processor-implemented method of claim 1, wherein the data store is a text document, and wherein the processor-implemented method further comprises:

searching, by the processor, the text document for text data that is part of the synthetic context-based object; and

associating the text document that contains said text data with the synthetic context-based object.

6. The processor-implemented method of claim 1, wherein the data store is a video file, and wherein the processor-implemented method further comprises:

searching, by the processor, metadata associated with the video file for text data that is part of the synthetic context-based object; and

associating the video file having said metadata with the synthetic context-based object.

7. The processor-implemented method of claim 1, wherein the data store is a web page, and wherein the processor-implemented method further comprises:

searching, by the processor, the web page for text data that is part of the synthetic context-based object; and

associating the web page that contains said text data with the synthetic context-based object.

8. The processor-implemented method of claim 1, further comprising:

receiving the request from the requester via a request pointer, wherein the request pointer points to a user-specified synthetic context-based object.

9. A computer program product for generating and utilizing a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising:

associating a non-contextual data object with a context object to define a synthetic context-based object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object;

associating the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is associated with data contained in the non-contextual data object and the context object; and

constructing a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same non-contextual data object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different context objects.

10. The computer program product of claim 9, wherein the program code is further readable and executable by the processor for:

data mining a data structure for the non-contextual data object and the context object, wherein said data mining locates said at least one specific data store that comprises data contained in the non-contextual data object and the context object.

11. The computer program product of claim 9, wherein the specific subject-matter for a particular data store in the data structure is exclusive to only said particular data store.

12. The computer program product of claim 9, wherein the specific subject-matter for a particular data store in the data structure overlaps a subject-matter of another data store in the data structure.

13. The computer program product of claim 9, wherein the data store is a text document, and wherein the program code is further readable and executable by the processor to:

search the text document for text data that is part of the synthetic context-based object; and

associate the text document that contains said text data with the synthetic context-based object.

14. The computer program product of claim 9, wherein the data store is a video file, and wherein the program code is further readable and executable by the processor to:

search metadata associated with the video file for text data that is part of the synthetic context-based object; and

associate the video file having said metadata with the synthetic context-based object.

15. The computer program product of claim 9, wherein the data store is a web page, and wherein the program code is further readable and executable by the processor to:

search the web page for text data that is part of the synthetic context-based object; and

associate the web page that contains said text data with the synthetic context-based object.

16. The computer program product of claim 9, wherein the program code is further readable and executable by the processor to:

receive the request from the requester via a request pointer, wherein the request pointer points to a user-specified synthetic context-based object.

17. A computer system comprising:

a processor, a computer readable memory, and a computer readable storage medium;

first program instructions to associate a non-contextual data object with a context object to define a synthetic context-based object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object;

second program instructions to associate the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is associated with data contained in the non-contextual data object and the context object; and

third program instructions to construct a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same context object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different non-contextual data objects; and

wherein

the first, second, and third program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.

18. The computer system of claim 17, further comprising:

fourth program instructions to data mine a data structure for the non-contextual data object and the context object, wherein said data mining locates said at least one specific data store that comprises data contained in the non-contextual data object and the context object; and

wherein

the fourth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.

19. The computer system of claim 17, wherein the specific subject-matter for a particular data store in the data structure is exclusive to only said particular data store.

20. The computer system of claim 17, wherein the specific subject-matter for a particular data store in the data structure overlaps a subject-matter of another data store in the data structure.