4. The processor-implemented method of claim 1 , further comprising: determining, by one or more hardware processors, a likelihood that a particular security-enabled synthetic context-based object is pulled into an appropriate security-enabled context-based data gravity well according to a Bayesian probability formula of: P ⁡ ( A ❘ B ) = P ⁡ ( B ❘ A ) ⁢ P ⁡ ( A ) P ⁡ ( B ) where: P(A|B) is the probability that a security-enabled synthetic context object will be an appropriate populator of a particular security-enabled context-based data gravity well (A) given that (|) a predefined amount of confirmed context objects are applied to the non-contextual data object in a security-enabled synthetic context-based object (B); P(B|A) is the probability that the predefined amount of confirmed context-based objects are applied to the non-contextual data object in the security-enabled synthetic context-based object (B) given that (|) the security-enabled synthetic context-based object is assigned to the particular security-enabled context-based data gravity well (A); P(A) is the probability that the particular security-enabled synthetic context-based object will be the appropriate populator of the particular security-enabled context-based data gravity well regardless of any other information; and P(B) is the probability that the particular security-enabled synthetic context-based object will have the predefined amount of confirmed context objects regardless of any other information.
1. A processor-implemented method of defining multiple security-enabled context-based data gravity wells on a security-enabled context-based data gravity wells membrane, the processor-implemented 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, by one or more hardware processors, 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; associating, by one or more hardware processors, the synthetic context-based object with a security object to generate a security-enabled synthetic context-based object, wherein the security object describes a circumstance that describes an environment in which an event is occurring; parsing, by one or more hardware processors, the security-enabled 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, a weighting factor of importance of the security-enabled synthetic context-based object, and a probability that the security object has been associated with a correct synthetic context-based object; calculating, by one or more hardware processors, a virtual mass of the parsed security-enabled synthetic context-based object, wherein the virtual mass of the parsed security-enabled synthetic context-based object is derived from a formula of: 
 (P(C)+P(S))×Wt(S), where P(C) is the probability that the non-contextual data object has been associated with the correct context object, wherein P(S) is the probability that the security object has been associated with the correct synthetic context-based object, and where Wt(S) is the weighting factor of importance of the security-enabled synthetic context-based object; creating, by one or more hardware processors, multiple security-enabled context-based data gravity well frameworks on a security-enabled context-based data gravity wells membrane, wherein each of the multiple security-enabled context-based data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one security object, and wherein the security-enabled context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple security-enabled context-based data gravity wells; transmitting multiple parsed security-enabled synthetic context-based objects to the security-enabled context-based data gravity wells membrane; defining, by said one or more hardware processors, multiple security-enabled context-based data gravity wells according to the virtual mass of multiple parsed security-enabled synthetic context-based objects that are pulled into each of the security-enabled context-based data gravity well frameworks, wherein each of the multiple parsed security-enabled synthetic context-based objects is pulled into a particular security-enabled context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object, said at least one context object, and said at least one security object in said particular security-enabled context-based data gravity well; and in response to an unmatched parsed security-enabled synthetic context-based object failing to be pulled into any of the security-enabled context-based data gravity wells, trapping, by one or more hardware processors, said unmatched parsed security-enabled synthetic context-based object in an unmatched parsed security-enabled synthetic context-based object trap.