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 how many parsed synthetic context-based objects are pulled into the particular context-based conformed dimensional data gravity well.

Abstract: A processor-implemented method, system, and/or computer program product mitigate subjectively disturbing content. A context-based data gravity wells membrane supports one or more gravity wells, which hold subjectively disturbing synthetic context-based objects made up of a non-contextual data object, a first context object, and a second context object. The first context object defines the non-contextual data object, and the second context object describes how subjectively disturbing content generated by combining the non-contextual data object and the first context object is according to predefined parameters described by the second context object. New content is passed across the context-based data gravity wells membrane.

Abstract: A method, system, and/or computer program product allocates computer processing work. One or more processors identify: an input data that is stored in a first computer for processing by a computer program; a virtual machine, stored in a second computer, that is capable of executing the computer program; a first set of constraint rules against moving the input data from the first computer; and a second set of constraint rules against moving the virtual machine from the second computer. The one or more processors assign a weight to each constraint rule, and sum the weight of all constraint rules that are applicable. In response to the first total constraint rule weight exceeding the second total constraint rule weight, movement of the input data from the first computer to the second computer is prohibited and the virtual machine is moved from the second computer to the first computer.

Abstract: A scoped search engine is disclosed. The scoped search engine includes a memory unit storing reference data records. The scoped search engine also includes a data comparison unit that searches the reference data records using different searches. The scoped search engine further includes a match analysis unit that combines result data from the different searches and determines a scope for a subsequent search based on the combined result data.

Abstract: A sensor system comprises: an energy storage device; an intermittent energy release device electrically coupled to the energy storage device, wherein the intermittent energy release device causes the energy storage device to release stored energy intermittently; a sensor electrically coupled to the energy storage device; a register electrically coupled to the sensor, wherein the register stores readings from the sensor; a synaptic neural network core electrically coupled to the sensor, wherein the synaptic neural network core converts the readings from the sensor into a synthetic context-based object that is derived from the readings and a context object; a transponder electrically coupled to the synaptic neural network core; and a storage buffer within the transponder, wherein the storage buffer stores the synthetic context-based object for transmission by the transponder to a monitoring system.

Abstract: A processor-implemented method, system, and/or computer program product generates and utilizes a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects. A non-contextual data object is associated with a 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 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 dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects is constructed for handling requests for data stores.

Abstract: A context-based graphical database system for storing a data structure comprises an entity node that stores an identifier of an entity. A data node stores non-contextual entity data about the entity. At least one context node is linked to the data node. The at least one context node provides context to the non-contextual entity data in the data node to create contextual entity data. A pointer from the entity node to the data node returns a data node identifier to the entity node to link the contextual entity data to the entity.

Abstract: A processor-implemented method, system, and/or computer program product generates and utilizes a synthetic context-based object to locate a user-specific data store. A processor associates a non-contextual data object with a context object to define a synthetic context-based object. The synthetic context-based object is associated with at least one specific data store that contains data that is associated with data in the non-contextual data object and in the context object. The processor determines a subject-matter of interest for a specific user, and associates that determined subject-matter of interest to a specific synthetic context-based object. In response to receiving a request for data from a specific user, the request is directed to the specific synthetic context-based object that describes the subject-matter of interest for that specific user. This specific synthetic context-based object locates the appropriate data store in order to return the requested data to the specific user.

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 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 navigates through a database. A non-contextual data object, which ambiguously relates to multiple subject-matters, is associated with a context object to define a synthetic context-based object. 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 associated with a first data node and a second data node within a database. In response to receiving a request for identifying and retrieving data from a data node that has a same context as the first data node, data from the second data node is retrieved.

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 method, computer program product and system for generating and maintaining synthetic context events. The steps include searching a data structure of synthetic context-based objects and associated data for a pattern of context exhibited at a first specified frequency within a first specified time period; combining the synthetic context-based objects and associated data exhibiting the pattern of context exhibited at the first specified frequency within the first specified time period into a synthetic context event; and optimizing and maintaining the synthetic context event by searching the data structure for additional synthetic context-based objects and associated data exhibiting a same pattern of context at a second specified time period different than the first specified time period and adding the additional synthetic context-based objects and associated data to the synthetic context event.

Abstract: A method, system, and/or computer program product allocates computer processing work. One or more processors identify: an input data that is stored in a first computer for processing by a computer program; a virtual machine, stored in a second computer, that is capable of executing the computer program; a first set of constraint rules against moving the input data from the first computer; and a second set of constraint rules against moving the virtual machine from the second computer. The one or more processors assign a weight to each constraint rule, and sum the weight of all constraint rules that are applicable. In response to the first total constraint rule weight exceeding the second total constraint rule weight, movement of the input data from the first computer to the second computer is prohibited and the virtual machine is moved from the second computer to the first computer.

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, which contains multiple instances of the non-contextual data object, according to a profile of a particular user. 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 a data store in a data structure that contains heterogeneous data stores having different formats.

Abstract: A processor-implemented method, system, and/or computer program product allocates computer processing work. Input data, which has been deemed to be in need of processing, is stored in a first computer. A virtual machine that is capable of processing the input data is stored on a second computer. A first set of constraint rules contains constraint rules against moving the input data from the first computer, and a second set of constraint rules contains constraint rules against moving the virtual machine from the second computer. Based on the first and second constraint rules, either the virtual machine is moved to the first computer or the input data is moved to the second computer.

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 a data store in a data structure that contains heterogeneous data stores that hold data of different formats.

Abstract: A processor-implemented method, system, and/or computer program product generates and utilizes a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects. A non-contextual data object is associated with a 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 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 dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects is constructed for handling requests for data stores.

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 how many parsed synthetic context-based objects are pulled into the particular context-based conformed dimensional data gravity well.

Abstract: A processor-implemented method, system, and/or computer program product generate and utilize a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects. A non-contextual data object is associated with a 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. A dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects is then constructed for handling requests for data stores.