Automatically create abstractions of large sets of data and then probabilistic inferences based on the abstractions. The probabilistic inference is derived from the logical hierarchy using Bayesian statistics to infer a probabilistic event based upon a characteristic of the data in a hierarchy of synthetic events. The logical hierarchy of a set of a plurality of synthetic events is related by at least one characteristic of data is built by accessing a first set of data. The first set of data is organized based on a first characteristic. A second set of data different than the first set of data is accessed. A second set of data based is organized based on a second characteristic. The first characteristic and the second characteristic are processed to generate a synthetic event. The synthetic event is a third set of data representing a result of a mathematical computation defined by an operation S(p1)==>F(p2).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method to compute a probabilistic inference comprising: building a logical hierarchy of a set of a plurality of synthetic events related by at least one characteristic of data by accessing a first set of data; organizing the first set of data based on a first characteristic; accessing a second set of data different than the first set of data; organizing the second set of data based on a second characteristic; processing the first characteristic and the second characteristic to generate a synthetic event, wherein the synthetic event comprises a third set of data representing a result of a mathematical computation defined by an operation S(p1)==>F(p2), wherein S comprises a set of input facts with probability p1, wherein the set of input facts comprise the first characteristic and the second characteristic, wherein F comprises an inferred event with probability p2, wherein the term “event” means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first characteristic, the second characteristic, and the synthetic event all comprise different events; and after the logical hierarchy of the set of a plurality of synthetic events is built, deriving from the logical hierarchy using Bayesian statistics to infer at least one probabilistic event based upon the characteristic of the data. 2. The computer-implemented method of claim 1, wherein the characteristic includes at least one temporal attribute. 3. The computer-implemented method of claim 1, wherein the characteristic includes at least one geographic attribute. 4. The computer-implemented method of claim 1, wherein the one or more of the synthetic events are grouped into one or more categories with a sum of each of the synthetic events in a category equal to one. 5. The computer-implemented method of claim 1, wherein the one or more of the synthetic events are distinct events represented by separate processes. 6. The computer-implemented method of claim 1, wherein the one or more of the synthetic events occur within a given time period. 7. The computer-implemented method of claim 1, wherein the accessing the first set of data from one or more physical sensors related to at least one user are data measurements of the user's geographical location. 8. The computer-implemented method of claim 1, wherein the accessing the first set of data from one or more physical sensors related to at least one user are data measurements of the user's biomedical data. 9. The computer-implemented method of claim 1, further comprising: evaluating against a threshold that the at least one probalistic event has been reached; and based the threshold being reached, triggering a transducer to take a physical action. 10. A computer program product for computing a probabilistic inference, the product having a computer readable non-transitory storage medium comprising program code operable for: building a logical hierarchy of a set of a plurality of synthetic events related by at least one characteristic of data by accessing a first set of data; organizing the first set of data based on a first characteristic; accessing a second set of data different than the first set of data; organizing the second set of data based on a second characteristic; processing the first characteristic and the second characteristic to generate a synthetic event, wherein the synthetic event comprises a third set of data representing a result of a mathematical computation defined by an operation S(p1)==>F(p2), wherein S comprises a set of input facts with probability p1, wherein the set of input facts comprise the first characteristic and the second characteristic, wherein F comprises an inferred event with probability p2, wherein the term “event” means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first characteristic, the second characteristic, and the synthetic event all comprise different events; and after the logical hierarchy of the set of a plurality of synthetic events is built, deriving from the logical hierarchy using Bayesian statistics to infer at least one probalistic event based upon the characteristic of the data. 11. The computer program product of claim 10, wherein the characteristic includes at least one temporal attribute. 12. The computer program product of claim 10, wherein the characteristic includes at least one geographic attribute. 13. The computer program product of claim 10, wherein the one or more of the synthetic events are grouped into one or more categories with a sum of each of the synthetic events in a category equal to one. 14. The computer program product of claim 10, wherein the one or more of the synthetic events are distinct events represented by separate processes. 15. The computer program product of claim 10, wherein the one or more of the synthetic events occur within a given time period. 16. The computer program product of claim 10, wherein the accessing the first set of data from one or more physical sensors related to at least one user are data measurements of the user's geographical location. 17. The computer-implemented method of claim 10, wherein the accessing the first set of data from one or more physical sensors related to at least one user are data measurements of the user's biomedical data. 18. The computer-implemented method of claim 10, further comprising: evaluating against a threshold that the at least one probalistic event has been reached; and based the threshold being reached, triggering a transducer to take a physical action. 19. A data processing system comprising: a bus; a processor connected to the bus; a memory connected to the bus, the memory storing instructions for carrying out a computer implemented method, wherein the processor is capable of carrying out the instructions of: building a logical hierarchy of a set of a plurality of synthetic events related by at least one characteristic of data by accessing a first set of data; organizing the first set of data based on a first characteristic; accessing a second set of data different than the first set of data; organizing the second set of data based on a second characteristic; processing the first characteristic and the second characteristic to generate a synthetic event, wherein the synthetic event comprises a third set of data representing a result of a mathematical computation defined by an operation S(p1)==>F(p2), wherein S comprises a set of input facts with probability p1, wherein the set of input facts comprise the first characteristic and the second characteristic, wherein F comprises an inferred event with probability p2, wherein the term “event” means a particular set of data that represents, encodes, or records at least one of a thing or happening, and wherein each of the first set of data, the second set of data, the first characteristic, the second characteristic, and the synthetic event all comprise different events; and after the logical hierarchy of the set of a plurality of synthetic events is built, deriving from the logical hierarchy using Bayesian statistics to infer at least one probalistic event based upon the characteristic of the data. 20. The data processing system of claim 19, wherein the characteristic includes at least one temporal attribute.