Abstract: Various mechanisms are provided for navigating among visualizations of quantitative data. At least one relationship is defined among a plurality of visualizations. A virtual multi-faceted shape is constructed, having a plurality of surfaces, some or all of which may correspond to visualizations. Visualizations may be presented, for example, by projecting or texture mapping the visualizations on corresponding surfaces of the shape. In at least one embodiment, surfaces are spatially oriented with one another in a manner that represents a relationship between the corresponding two visualizations. The user can interact with the virtual shape, for example by causing it to rotate, zoom, move, or the like. Such interactions cause different visualizations to be displayed.

Abstract: Various mechanisms are described for generating metadata describing relationships among data sets. Quantitative data can be analyzed to determine relationships, and metadata representing the determined relationships can then be stored. Visualizations can then be generated from the metadata, and a navigational model can be defined based on the generated set of visualizations. The navigational models can provide robust visual mechanics for implementing intuitive navigational schemes that facilitate interaction with data on any suitable output device, include small screens as found on smartphones and/or tablets.

Abstract: A computer system, includes a display interface; a user input device interface; and a processor cooperatively operable with the display interface and the user input device interface. The processor is configured to interacting with a user to selectively browse, via the user input device interface and the display interface, relationship dependencies between a selected mashup or service and mashup assets one level removed from the selected mashup or service.

Abstract: A system for continuous analytics comprises an in-memory storage and a processor. The processor receives a query that indicates an analytic function to be performed on a combination of static data and real-time data. The processor loads the in-memory storage from a first source external to the processor with data that was stored as the static data. At the same time that the in-memory storage is already loaded with the static data, the processor continuously receives ephemeral real-time data as it is being generated by a second source external to the processor. The processor runs the analytic function from the query against the in-memory storage loaded with the first data and the real-time data that is being continuously received, in combination, to produce a result of the analytic function. The result is stored and time-stamped in the in-memory storage as an analytic cube.

Abstract: Various mechanisms are provided for navigating among visualizations of quantitative data. At least one relationship is defined among a plurality of visualizations. A virtual multi-faceted shape is constructed, having a plurality of surfaces, some or all of which may correspond to visualizations. Visualizations may be presented, for example, by projecting or texture mapping the visualizations on corresponding surfaces of the shape. In at least one embodiment, surfaces are spatially oriented with one another in a manner that represents a relationship between the corresponding two visualizations. The user can interact with the virtual shape, for example by causing it to rotate, zoom, move, or the like. Such interactions cause different visualizations to be displayed.

Abstract: Various mechanisms are described for generating metadata describing relationships among data sets. Quantitative data can be analyzed to determine relationships, and metadata representing the determined relationships can then be stored. Visualizations can then be generated from the metadata, and a navigational model can be defined based on the generated set of visualizations. The navigational models can provide robust visual mechanics for implementing intuitive navigational schemes that facilitate interaction with data on any suitable output device, include small screens as found on smartphones and/or tablets.

Abstract: A system for continuous analytics comprises an in-memory storage and a processor. The processor receives a query that indicates an analytic function to be performed on a combination of static data and real-time data. The processor loads the in-memory storage from a first source external to the processor with data that was stored as the static data. At the same time that the in-memory storage is already loaded with the static data, the processor continuously receives ephemeral real-time data as it is being generated by a second source external to the processor. The processor runs the analytic function from the query against the in-memory storage loaded with the first data and the real-time data that is being continuously received, in combination, to produce a result of the analytic function. The result is stored and time-stamped in the in-memory storage as an analytic cube.

Abstract: A computer system includes a display interface, a user input device interface, and a processor cooperatively operable with the display interface and the user input device interface. The processor is configured to facilitate displaying, via the display interface, a mashup or service; interacting with the user to input, via the user input device interface, an indication to take a snapshot of the mashup or service being currently displayed via the display interface; and storing a snapshot artifact of live data from the mashup or service at the instant the snapshot is taken.

Abstract: A computer system includes a display interface, a user input device interface, and a processor cooperatively operable with the display interface and the user input device interface. The processor is configured to facilitate displaying, via the display interface, a mashup or service; interacting with the user to input, via the user input device interface, an indication to take a snapshot of the mashup or service being currently displayed via the display interface; and storing a snapshot artifact of live data from the mashup or service at the instant the snapshot is taken.

Abstract: A system that includes a shared variable scope configured to store at least one shared variable binding, a first interpreter configured to search the shared variable scope to determine a variable binding for a variable, and a second interpreter configured to search the shared variable scope to determine the variable binding for the variable.

Abstract: A method for analyzing a target system that includes obtaining a plurality of characteristics from the target system using a characteristics extractor, wherein the plurality of characteristics is associated with a characteristics model, storing each of the plurality of characteristics in a characteristics store using a tracking mechanism, and analyzing the target system by issuing a query to the characteristics store to obtain an analysis result, wherein the query uses tracking information associated with the tracking mechanism.

Abstract: A method for analyzing a target system that includes obtaining a characteristics model, generating at least one selected from the group consisting of a schema, characteristics store API, and a characteristics extractor, using the characteristics model, obtaining a plurality of characteristics from the target system using characteristics extractor, wherein each of the plurality of characteristics is associated with the characteristics model, storing each of the plurality of characteristics in the characteristics store using the schema, and analyzing the target system by issuing at least one query to the characteristics store to obtain an analysis result.

Abstract: A method for analyzing a target system that includes generating a characteristics model using a schema defining a domain, obtaining a plurality of characteristics from the target system using a characteristics extractor, wherein the plurality of characteristics is associated with the characteristics model storing each of the plurality of characteristics in a characteristics store, and analyzing the target system by issuing at least one query to the characteristics store to obtain an analysis result.

Abstract: A method for analyzing a target system that includes obtaining a characteristics model, loading the characteristics model into a meta model, obtaining a plurality of characteristics from the target system using a characteristics extractor, wherein each of the plurality of characteristics is associated with the characteristics model, storing each of the plurality of characteristics obtained from the target system in a characteristics store, and analyzing the target system by issuing at least one query to the characteristics store to obtain an analysis result, wherein the issuing the at least one query comprises verifying the at least one query using the meta model.

Abstract: A method for analyzing a target system that includes obtaining a plurality of characteristics from the target system using a characteristics extractor and at least one selected from the group consisting of a software build project associated with the target system and a modified software build project associated with the target system, wherein the plurality of characteristics is associated with a characteristics model, storing each of the plurality of characteristics in a characteristics store, and analyzing the target system by issuing at least one query to the characteristics store to obtain an analysis result.

Abstract: A method for analyzing a target system that includes obtaining a plurality of characteristics from the target system using a characteristics extractor, wherein the plurality of characteristics is associated with a characteristics model, storing each of the plurality of characteristics in a characteristics store using a tracking mechanism, and analyzing the target system by issuing a query to the characteristics store to obtain an analysis result, wherein the query uses tracking information associated with the tracking mechanism.

Abstract: A method for analyzing a target system that includes obtaining a plurality of characteristics from the target system using a characteristics extractor, wherein the plurality of characteristics is associated with a characteristics model, storing each of the plurality of characteristics in a characteristics store, and analyzing the target system by issuing at least one query to the characteristics store to obtain an analysis result.

Abstract: A method for analyzing a target system, that includes obtaining a plurality of characteristics from the target system using a characteristics extractor, wherein the plurality of characteristics is defined in a characteristics model and each of the plurality of characteristics is associated with one of a plurality of artifacts defined in the characteristics model, storing each of the plurality of characteristics in a characteristics store, and analyzing the target system by issuing a query to the characteristics store to obtain an analysis result, wherein the query is used to determine the presence of a first pattern in the target system.

Abstract: A method for analyzing a target system that includes obtaining a characteristics model, generating at least one selected from the group consisting of a schema, characteristics store API, and a characteristics extractor, using the characteristics model, obtaining a plurality of characteristics from the target system using characteristics extractor, wherein each of the plurality of characteristics is associated with the characteristics model, storing each of the plurality of characteristics in the characteristics store using the schema, and analyzing the target system by issuing at least one query to the characteristics store to obtain an analysis result.

Abstract: A method for analyzing a target system that includes obtaining a characteristics model, loading the characteristics model into a meta model, obtaining a plurality of characteristics from the target system using a characteristics extractor, wherein each of the plurality of characteristics is associated with the characteristics model, storing each of the plurality of characteristics obtained from the target system in a characteristics store, and analyzing the target system by issuing at least one query to the characteristics store to obtain an analysis result, wherein the issuing the at least one query comprises verifying the at least one query using the meta model.