Context-based data gravity wells
First Claim
1. A processor-implemented method of defining multiple context-based data gravity wells on a context-based data gravity wells membrane, wherein the context-based data gravity wells membrane is a mathematical framework for a data structure, 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 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;
parsing, by the processor, the synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said 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, by the processor, a virtual mass of a parsed synthetic context-based object, wherein the virtual mass of the parsed synthetic context-based object is derived from a formula of;
P(C)×
Wt(S),where P(C) is the probability that the non-contextual data object has been associated with the correct context object, and where Wt(S) is the weighting factor of importance of the synthetic context-based object;
creating, by the processor, multiple context-based data gravity well frameworks on a context-based data gravity wells membrane, wherein each of the multiple context-based data gravity well frameworks comprises at least one non-contextual data object and at least one context object, and wherein the context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based data gravity wells;
transmitting, by the processor, multiple parsed synthetic context-based objects to the context-based data gravity wells membrane; and
defining, by the processor, multiple context-based data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects that are pulled into each of the context-based data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects is pulled into a particular context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object and said at least one context object in said particular context-based data gravity well.
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Abstract
A processor-implemented method, system, and/or computer program product defines multiple context-based data gravity wells on a context-based data gravity wells membrane. Non-contextual data objects are associated with context objects to define synthetic context-based objects. The synthetic context-based objects are parsed into an n-tuple that includes 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. A virtual mass of each parsed synthetic context-based object is calculated, in order to define a shape of multiple context-based data gravity wells that are created when synthetic context-based objects are pulled into each of the context-based data gravity well frameworks on a context-based data gravity wells membrane.
181 Citations
20 Claims
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1. A processor-implemented method of defining multiple context-based data gravity wells on a context-based data gravity wells membrane, wherein the context-based data gravity wells membrane is a mathematical framework for a data structure, the processor-implemented method comprising:
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receiving, by a processor, a data stream of non-contextual data objects, wherein each of the non-contextual data objects 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; parsing, by the processor, the synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said 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, by the processor, a virtual mass of a parsed synthetic context-based object, wherein the virtual mass of the parsed synthetic context-based object is derived from a formula of;
P(C)×
Wt(S),where P(C) is the probability that the non-contextual data object has been associated with the correct context object, and where Wt(S) is the weighting factor of importance of the synthetic context-based object; creating, by the processor, multiple context-based data gravity well frameworks on a context-based data gravity wells membrane, wherein each of the multiple context-based data gravity well frameworks comprises at least one non-contextual data object and at least one context object, and wherein the context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based data gravity wells; transmitting, by the processor, multiple parsed synthetic context-based objects to the context-based data gravity wells membrane; and defining, by the processor, multiple context-based data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects that are pulled into each of the context-based data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects is pulled into a particular context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object and said at least one context object in said particular context-based data gravity well. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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4. The processor-implemented method of claim 1, wherein the weighting factor of importance of the synthetic context-based object is based on how important the synthetic context-based object is to a particular project.
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5. The processor-implemented method of claim 1, wherein the correct context object is a context object that defines the specific subject-matter of a particular project.
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6. The processor-implemented method of claim 1, further comprising:
graphically representing, by the processor, said at least one context object on a wall of said particular context-based data gravity well.
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7. The processor-implemented method of claim 1, further comprising:
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determining, by the processor, an age of each of the multiple parsed synthetic context-based objects that have been pulled into the particular context-based data gravity well; and removing from the particular context-based data gravity well any parsed synthetic context-based object that is older than a predetermined age.
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8. A computer program product for defining multiple context-based data gravity wells on a context-based data gravity wells membrane, wherein the context-based data gravity wells membrane is a mathematical framework for a data structure, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising:
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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; parsing the synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said 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, wherein the virtual mass of the parsed synthetic context-based object is derived from a formula of;
P(C)×
Wt(S),where P(C) is the probability that the non-contextual data object has been associated with the correct context object, and where Wt(S) is the weighting factor of importance of the synthetic context-based object; creating multiple context-based data gravity well frameworks on a context-based data gravity wells membrane, wherein each of the multiple context-based data gravity well frameworks comprises at least one non-contextual data object and at least one context object, and wherein the context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based data gravity wells; transmitting multiple parsed synthetic context-based objects to the context-based data gravity wells membrane; and defining multiple context-based data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects that are pulled into each of the context-based data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects is pulled into a particular context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object and said at least one context object in said particular context-based data gravity well. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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11. The computer program product of claim 8, wherein the weighting factor of importance of the synthetic context-based object is based on how important the synthetic context-based object is to a particular project.
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12. The computer program product of claim 8, wherein the correct context object is a context object that defines the specific subject-matter of a particular project.
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13. The computer program product of claim 8, further comprising program code that is readable and executable by the processor to:
graphically represent said at least one context object on a wall of said particular context-based data gravity well.
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14. The computer program product of claim 8, further comprising program code that is readable and executable by the processor to:
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determine an age of each of the multiple parsed synthetic context-based objects that have been pulled into the particular context-based data gravity well; and remove from the particular context-based data gravity well any parsed synthetic context-based object that is older than a predetermined age.
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15. A computer system comprising:
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a processor, a computer readable memory, and a 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 parse the synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said 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; fourth program instructions to calculate a virtual mass of a parsed synthetic context-based object, wherein the virtual mass of the parsed synthetic context-based object is derived from a formula of;
P(C)×
Wt(S),where P(C) is the probability that the non-contextual data object has been associated with the correct context object, and where Wt(S) is the weighting factor of importance of the synthetic context-based object; fifth program instructions to create multiple context-based data gravity well frameworks on a context-based data gravity wells membrane, wherein the context-based data gravity wells membrane is a mathematical framework for a data structure, wherein each of the multiple context-based data gravity well frameworks comprises at least one non-contextual data object and at least one context object, and wherein the context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based data gravity wells; sixth program instructions to transmit multiple parsed synthetic context-based objects to the context-based data gravity wells membrane; and seventh program instructions to define multiple context-based data gravity wells according to the mass of multiple parsed synthetic context-based objects that are pulled into each of the context-based data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects is pulled into a particular context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object and said at least one context object in said particular context-based data gravity well; and
whereinthe first, second, third, fourth, fifth, sixth, and seventh program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory. - View Dependent Claims (16, 17, 18, 19, 20)
the eighth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.
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17. The computer system of claim 15, further comprising:
eighth program instructions to determine a likelihood that a particular synthetic context-based object is pulled into an appropriate context-based data gravity well according to a Bayesian probability formula of;
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18. The computer system of claim 15, wherein the correct context object is a context object that defines the specific subject-matter of a particular project.
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19. The computer system of claim 15, further comprising:
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eighth program instructions to graphically represent said at least one context object on a wall of said particular context-based data gravity well; and
whereinthe eighth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.
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20. The computer system of claim 15, further comprising:
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eighth program instructions to determine an age of each of the multiple parsed synthetic context-based objects that have been pulled into the particular context-based data gravity well; and ninth program instructions to remove from the particular context-based data gravity well any parsed synthetic context-based object that is older than a predetermined age; and
whereinthe eighth and ninth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.
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Specification