Abstract: A processor-implemented method, system, and/or computer program product measures and displays facets in context-based conformed dimensional data gravity wells. 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 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. Data from the multiple context-based conformed dimensional data gravity wells then populates nodes in a data model. A displayed appearance of the particular context-based conformed dimensional data gravity well is dynamically adjusted according to the how closely each of the multiple parsed synthetic context-based objects matches said at least one context object and/or at least one dimension object.

Abstract: A processor-implemented method, system, and/or computer program product derives and utilizes a context object to generate a synthetic context-based object. A context object for a non-contextual data object is derived by contextually searching a document that contains multiple instances of the non-contextual data object. The non-contextual data object is associated with the derived 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.

Abstract: A method, system, and computer program product for minimizing surprisal context data. The method includes the steps of: identifying characteristics of a data event; receiving an input of rank of at least two identified characteristics of the data event; generating a hierarchy of ranked, identified characteristics based on the rank of the identified characteristics of the data event; and comparing the hierarchy of ranked, identified characteristics to a repository of characteristic context patterns. If at least one reference artifact of the characteristic context pattern matches the hierarchy of ranked, identified characteristics, the characteristic context pattern is broken into pieces, storing the pieces that matched the hierarchy. The stored pieces are then combined into a surprisal context filter pattern with context determined to be probabilistically present within a specified degree of certainty in a data input and compared to a data input of data events to detect anomalous events.

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.

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.

Abstract: A context-based graph-relational intersect derived (CB-GRID) links a real entity graph node to a synthetic entity graph node. The real entity graph node comprises a pointer to a primary key in a first tuple that non-contextually describes the real entity. A primary relational database comprises the first tuple that non-contextually describes the real entity. A context relational database comprises a second tuple that contains a foreign key that matches the primary key in the primary relational database. The second tuple dynamically describes a context of data in the first tuple. A contextual entity relational database comprises a third tuple that contains data from the first tuple and the second tuple. The synthetic entity graph node describes a synthetic entity that is described by data in the third tuple, such that the contextual entity relational database links the real entity graph node to the synthetic entity graph node.

Abstract: A processor-implemented method, system, and/or computer program product securely accesses a specific data store. A non-contextual data object is associated with a context object to define a first synthetic context-based object. The non-contextual data object ambiguously describes multiple types of persons, and the context object provides a circumstantial context that identifies a specific type of person from the multiple types of persons. The first synthetic context-based object is associated with at least one specific data store in a data structure. A string of binary data that describes a requester of data, including a time window for receipt and security level of the requester, is received by a security module for generating a new synthetic context-based object. If there is a match between the new synthetic context-based object, the first synthetic context-based object, and the security level of the requester, then the data is returned to the requester.

Abstract: A processor-implemented method, system, and/or computer program product securely accesses a specific data store. A non-contextual data object is associated with a context object to define a first synthetic context-based object. The non-contextual data object ambiguously describes multiple types of persons, and the context object provides a circumstantial context that identifies a specific type of person from the multiple types of persons. The first synthetic context-based object is associated with at least one specific data store in a data structure. A string of binary data that describes a requester of data is received by a security module for generating a new synthetic context-based object. If there is a match between the new synthetic context-based object and the first synthetic context-based object, then the data is returned to the requester.

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.

Abstract: A method, system, and computer program product for minimizing surprisal context data. The method includes the steps of: identifying characteristics of a data event; receiving an input of rank of at least two identified characteristics of the data event; generating a hierarchy of ranked, identified characteristics based on the rank of the identified characteristics of the data event; and comparing the hierarchy of ranked, identified characteristics to a repository of characteristic context patterns. If at least one reference artifact of the characteristic context pattern matches the hierarchy of ranked, identified characteristics, the characteristic context pattern is broken into pieces, storing the pieces that matched the hierarchy. The stored pieces are then combined into a surprisal context filter pattern with context determined to be probabilistically present within a specified degree of certainty in a data input and compared to a data input of data events to detect anomalous events.

Abstract: A method, system, and computer program product for minimizing surprisal context data. The method comprising the steps of: identifying characteristics of a data event; receiving an input of rank of at least two identified characteristics of the data event; the computer generating a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics of the data event; and comparing the hierarchy of ranked, identified characteristics to a repository of reference artifacts arranged in characteristic context patterns. If at least one reference artifact arranged in characteristic context patterns from the repository matches the hierarchy of ranked, identified characteristics, the characteristic context pattern is stored as a surprisal context filter and compared to a data input of data events to detect anomalous events.

Abstract: A processor-implemented method, system, and/or computer program product generates and utilizes synthetic context-based objects. A non-contextual data object is associated with a context object, which comports with a predetermined set of constraints, 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 request for a data store that is associated with the synthetic context-based object results in the return of at least one data store that is associated with the synthetic context-based object.

Abstract: A processor-implemented method, system, and/or computer program product defines multiple security-enabled context-based data gravity wells on a security-enabled 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 associated with one or more security objects to generate security-enabled synthetic context-based objects, which 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, probability that the security object has been associated with a correct synthetic context-based object, and a weighting factor of importance of the security-enabled synthetic context-based object.

Abstract: A method (and system) includes providing a memory including a plurality of named locations each holding a value and introducing at least one freshener. The at least one freshener chooses one of the plurality of named locations and re-computes its value.

Abstract: A processor-implemented method, system, and/or computer program product defines multiple security-enabled context-based data gravity wells on a security-enabled 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 associated with one or more security objects to generate security-enabled synthetic context-based objects, which 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, probability that the security object has been associated with a correct synthetic context-based object, and a weighting factor of importance of the security-enabled synthetic context-based object.

Abstract: A processor-implemented method, system, and/or computer program product measures and displays facets in context-based conformed dimensional data gravity wells. 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 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. Data from the multiple context-based conformed dimensional data gravity wells then populates nodes in a data model. A displayed appearance of the particular context-based conformed dimensional data gravity well is dynamically adjusted according to the how closely each of the multiple parsed synthetic context-based objects matches said at least one context object and/or at least one dimension object.

Abstract: A processor-implemented method, system, and/or computer program product derives and utilizes a context object to generate a synthetic context-based object. A context object for a non-contextual data object is derived by contextually searching a document that contains multiple instances of the non-contextual data object. The non-contextual data object is associated with the derived 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.

Abstract: A processor-implemented method, system, and/or computer program product defines multiple dimensional data gravity wells on a conformed dimensional data gravity wells membrane. Non-dimensional data objects are associated with dimension objects to define conformed dimensional objects. The conformed dimensional objects are parsed into an n-tuple that includes a pointer to one of the non-dimensional data objects, a probability that a non-dimensional data object has been associated with a correct dimension object, and a weighting factor of importance of the conformed dimensional object. A virtual mass of each parsed conformed dimensional object is calculated, in order to define a shape of multiple dimensional data gravity wells that are created when conformed dimensional objects are pulled into each of the dimensional data gravity well frameworks on a conformed dimensional data gravity wells membrane.

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.

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.