Conformed dimensional and context-based data gravity wells

• US 9,251,246 B2
• Filed: 01/28/2015
• Issued: 02/02/2016
• Est. Priority Date: 01/02/2013
• 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., Linton, Jeb R.
• Primary Examiner(s): Mahmoudi, Tony
• Assistant Examiner(s): Le, Michael
• Application Number:
  US14/607,468
• Publication Number:
  US 20150149470A1
• Time in Patent Office: 370 Days
• Field of Search: 707/755
• US Class Current: 1/1
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/285 Clustering or classification

Abstract

A processor-implemented method, system, and/or computer program product defines multiple context-based conformed dimensional data gravity wells on a context-based conformed dimensional data gravity wells membrane. Conformed dimensional objects and synthetic context-based objects are parsed into n-tuples. A virtual mass of each parsed object is calculated, in order to define a shape of the multiple context-based conformed dimensional data gravity wells that are created when data objects that are pulled into each of the context-based conformed dimensional data gravity well frameworks on a context-based conformed dimensional gravity wells membrane.

Claims

1. A processor-implemented method of defining multiple context-based conformed dimensional data gravity wells on a context-based conformed dimensional data gravity wells membrane, the processor-implemented method comprising:

receiving, by a processor, a data stream of non-contextual data objects, wherein each of the non-contextual data objects is a numerical value that ambiguously relates to multiple subject-matters;

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

receiving, by the processor, a data stream of non-dimensional data objects;

applying, by the processor, a dimension object to one of the non-dimensional data objects to define a conformed dimensional object;

parsing, by the processor, the conformed dimensional object into a dimensional n-tuple, wherein the dimensional n-tuple comprises a pointer to one of the non-dimensional data objects and a probability that one of the non-dimensional data objects has been associated with a correct dimensional label;

parsing, by the processor, the synthetic context-based object into a context-based n-tuple, wherein the context-based n-tuple comprises a pointer to one of the non-contextual data objects and a probability that a non-contextual data object has been associated with a correct context object;

calculating, by the processor, a virtual mass of a parsed synthetic context-based object based on a probability that the non-contextual data object has been associated with a correct context object;

calculating, by the processor, a virtual mass of a parsed conformed dimensional object based on a probability that one of the non-dimensional data objects has come from a data source whose data has been predetermined to be appropriate for storage in a particular dimensional data gravity well;

creating, by the processor, multiple context-based conformed dimensional data gravity well frameworks on a context-based conformed dimensional data gravity wells membrane, wherein each of the multiple context-based conformed dimensional data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one dimension object, and wherein the context-based conformed dimensional data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based conformed dimensional data gravity wells;

transmitting, by the processor, multiple parsed synthetic context-based objects and multiple parsed conformed dimensional objects to the context-based conformed dimensional data gravity wells membrane; and

defining, by the processor, multiple context-based conformed dimensional data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects and the virtual mass of multiple parsed conformed dimensional objects that are pulled into each of the context-based conformed dimensional data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects and multiple parsed conformed dimensional objects is pulled into a particular context-based conformed dimensional data gravity well in response to values from its n-tuple matching said at least one context object or said at least one dimension object in said particular context-based conformed dimensional data gravity well.

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

graphically displaying the multiple context-based conformed dimensional data gravity wells according to a combined virtual mass of the multiple parsed synthetic context-based objects and the multiple parsed conformed dimensional objects, wherein a first context-based conformed dimensional data gravity well holds a more virtually massive combination of parsed data objects than a second context-based conformed dimensional data gravity well, and wherein the first context-based conformed dimensional data gravity well extends farther away from the context-based conformed dimensional data gravity wells membrane than the second context-based conformed dimensional data gravity well.

3. The processor-implemented method of claim 1, wherein a particular data object is either a conformed dimensional object or a synthetic context-based object, the processor-implemented method further comprising:

determining, by the processor, a likelihood that the particular data object is pulled into an appropriate context-based conformed dimensional data gravity well according to a Bayesian probability formula of;

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

determining that said one of the non-dimensional data objects is uncorrupted by determining that said one of the non-dimensional data objects is not a fragment of an original data object.

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

graphically representing, by the processor, said at least one dimension object and said at least one context object on a wall of said particular context-based conformed dimensional data gravity well.

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

determining, by the processor, an age of each data that has been pulled into the particular context-based conformed dimensional data gravity well; and

removing from the particular context-based conformed dimensional data gravity well any data object that is older than a predetermined age.

7. A computer program product for defining multiple context-based conformed dimensional data gravity wells on a context-based conformed dimensional data gravity wells membrane, 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:

receiving a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters;

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

receiving a data stream of non-dimensional data objects;

applying a dimension object to one of the non-dimensional data objects to define a conformed dimensional object;

parsing the conformed dimensional object into a dimensional n-tuple, wherein the dimensional n-tuple comprises a pointer to one of the non-dimensional data objects, a probability that one of the non-dimensional data objects is uncorrupted, and a weighting factor of importance of the conformed dimensional object;

parsing the synthetic context-based object into a context-based n-tuple, wherein the context-based n-tuple comprises a pointer to one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, and a weighting factor of importance of the synthetic context-based object;

calculating a virtual mass of a parsed synthetic context-based object based on a probability that the non-contextual data object has been associated with a correct context object;

calculating a virtual mass of a parsed conformed dimensional object based on a probability that one of the non-dimensional data objects has come from a data source whose data has been predetermined to be appropriate for storage in a particular dimensional data gravity well;

creating multiple context-based conformed dimensional data gravity well frameworks on a context-based conformed dimensional data gravity wells membrane, wherein each of the multiple context-based conformed dimensional data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one dimension object, and wherein the context-based conformed dimensional data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based conformed dimensional data gravity wells;

transmitting multiple parsed synthetic context-based objects and multiple parsed conformed dimensional objects to the context-based conformed dimensional data gravity wells membrane; and

defining multiple context-based conformed dimensional data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects and the virtual mass of multiple parsed conformed dimensional objects that are pulled into each of the context-based conformed dimensional data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects and multiple parsed conformed dimensional objects is pulled into a particular context-based conformed dimensional data gravity well in response to values from its n-tuple matching said at least one context object or said at least one dimension object in said particular context-based conformed dimensional data gravity well.

8. The computer program product of claim 7, further comprising program code that is readable and executable by the processor to:

graphically display the multiple context-based conformed dimensional data gravity wells according to a combined virtual mass of the multiple parsed synthetic context-based objects and the multiple parsed conformed dimensional objects, wherein a first context-based conformed dimensional data gravity well holds a more virtually massive combination of parsed data objects than a second context-based conformed dimensional data gravity well, and wherein the first context-based conformed dimensional data gravity well extends farther away from the context-based conformed dimensional data gravity wells membrane than the second context-based conformed dimensional data gravity well.

9. The computer program product of claim 7, wherein a particular data object is either a conformed dimensional object or a synthetic context-based object, and wherein the computer program product further comprises program code that is readable and executable by the processor to:

determine a likelihood that a particular data object is pulled into an appropriate context-based conformed dimensional data gravity well according to a Bayesian probability formula of;

10. The computer program product of claim 7, wherein the weighting factor of importance of a data object is based on how important the data object is to a particular project.

11. The computer program product of claim 7, further comprising program code that is readable and executable by the processor to:

determine that said one of the non-dimensional data objects is uncorrupted by determining that said one of the non-dimensional data objects is not a fragment of an original data object.

12. The computer program product of claim 7, further comprising program code that is readable and executable by the processor to:

graphically represent said at least one dimension object and said at least one context object on a wall of said particular context-based conformed dimensional data gravity well.

13. The computer program product of claim 7, further comprising program code that is readable and executable by the processor to:

determine an age of each data that has been pulled into the particular context-based conformed dimensional data gravity well; and

remove from the particular context-based conformed dimensional data gravity well any data object that is older than a predetermined age.

14. A computer system comprising:

a processor, a computer readable memory, and a non-transitory computer readable storage medium;

first program instructions to receive a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters;

second program instructions to associate one of the non-contextual data objects with a context object to define a synthetic context-based object, wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of said one of the non-contextual data objects;

third program instructions to receive a data stream of non-dimensional data objects;

fourth program instructions to apply a dimension object to one of the non-dimensional data objects to define a conformed dimensional object;

fifth program instructions to parse the conformed dimensional object into a dimensional n-tuple, wherein the dimensional n-tuple comprises a pointer to one of the non-dimensional data objects and a probability that one of the non-dimensional data objects is uncorrupted;

sixth program instructions to parse the synthetic context-based object into a context-based n-tuple, wherein the context-based n-tuple comprises a pointer to one of the non-dimensional data objects and a probability that one of the non-dimensional data objects is uncorrupted;

seventh program instructions to calculate a virtual mass of a parsed synthetic context-based object based on a probability that the non-contextual data object has been associated with a correct context object;

eighth program instructions to calculate a virtual mass of a parsed conformed dimensional object based on a probability that one of the non-dimensional data objects has come from a data source whose data has been predetermined to be appropriate for storage in a particular dimensional data gravity well;

ninth program instructions to create multiple context-based conformed dimensional data gravity well frameworks on a context-based conformed dimensional data gravity wells membrane, wherein each of the multiple context-based conformed dimensional data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one dimension object, and wherein the context-based conformed dimensional data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based conformed dimensional data gravity wells;

tenth program instructions to transmit multiple parsed synthetic context-based objects and multiple parsed conformed dimensional objects to the context-based conformed dimensional data gravity wells membrane; and

eleventh program instructions to define multiple context-based conformed dimensional data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects and the virtual mass of multiple parsed conformed dimensional objects that are pulled into each of the context-based conformed dimensional data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects and multiple parsed conformed dimensional objects is pulled into a particular context-based conformed dimensional data gravity well in response to values from its n-tuple matching said at least one context object or said at least one dimension object in said particular context-based conformed dimensional data gravity well; and

wherein

the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, and eleventh program instructions are stored on the non-transitory computer readable storage medium for execution by the processor via the computer readable memory.

15. The computer system of claim 14, further comprising:

twelfth program instructions to graphically display the multiple context-based conformed dimensional data gravity wells according to a combined virtual mass of the multiple parsed synthetic context-based objects and the multiple parsed conformed dimensional objects, wherein a first context-based conformed dimensional data gravity well holds a more virtually massive combination of parsed data objects than a second context-based conformed dimensional data gravity well, and wherein the first context-based conformed dimensional data gravity well extends farther away from the context-based conformed dimensional data gravity wells membrane than the second context-based conformed dimensional data gravity well; and

wherein the twelfth program instructions are stored on the non-transitory computer readable storage medium for execution by the processor via the computer readable memory.

16. The computer system of claim 14, wherein a particular data object is either a conformed dimensional object or a synthetic context-based object, and wherein the computer system further comprises:

twelfth program instructions to determine a likelihood that a particular data object is pulled into an appropriate context-based conformed dimensional data gravity well according to a Bayesian probability formula of;

17. The computer system of claim 14, wherein the weighting factor of importance of a data object is based on how important the data object is to a particular project.

18. The computer system of claim 14, further comprising:

twelfth program instructions to determine that said one of the non-dimensional data objects is uncorrupted by determining that said one of the non-dimensional data objects is not a fragment of an original data object; and

wherein

the twelfth program instructions are stored on the non-transitory computer readable storage medium for execution by the processor via the computer readable memory.

19. The computer system of claim 14, further comprising:

twelfth program instructions to determine an age of each data that has been pulled into the particular context-based conformed dimensional data gravity well; and

thirteenth program instructions to remove from the particular context-based conformed dimensional data gravity well any data object that is older than a predetermined age; and

wherein the twelfth and thirteenth program instructions are stored on the non-transitory computer readable storage medium for execution by the processor via the computer readable memory.