Abstract: A pattern of visual interactivity may be automatically proposed for processing a data set that exhibits dimensions of variability. A visual display component may be analyzed to assess whether it is capable of receiving user input representing changes that correspond to types of changes of the identified dimensions of the data set. A suitable visual display component may be provided as a suggestion on a user interface for potentially using the visual display component to allow a user to interact with the data. In some embodiments, a visual display component includes a number of visual objects and/or visually interactive components for use within a graphical user interface. A visual display component may include one or more analytic patterns having at least one of an equation, rule, constraint, expression or combination thereof that represents the analytic pattern. Alternatively, a visual display component may involve a visual display representation of data through visual characteristics of a graphical object.

Abstract: A complex solver that is able to solve a diverse variety of model analytics. Upon identifying the various input variable(s) and output variable(s) of the model variables, if a straight forward solve is not to be performed, it is determined whether the model analytics is to be inverted such that a forward solve can be performed. For instance, it might not be known generally, or at least to the solver, how to invert a particular equation of other model analytics. If the model analytics is to be inverted, the model analytics is inverted, and the forward solve is performed. On the other hand, if the inversion of the model analytics is not to be performed, then a numerical analysis solve is performed.

Abstract: Aspects described relate to analyzing data at a high level of abstraction. “Smarts,” incorporating one or more analytic patterns, may be applied to data sources. These Smarts may be in the form of at least one of an equation, rule, constraint or expression to generate a result from applying the analytic pattern to the data. Smarts may be pre-defined and selected from a library based on user preference and the data input. Any suitable user interface may be used to apply analytic pattern(s) to items of data as well as provide a visual environment for a user to make intuitive adjustments to the data, add controls in the user interface to filter or constrain the data, or combinations thereof, resulting in one or more modified results.

Abstract: Aspects discussed relate to creating a presentation of data through an arbitrary graphical object that has the capacity to visually represent data. Based on parameters of the visual characteristics and/or the data set, dimensions of data may be bound to appropriate visual characteristics of the graphical object to render a data visual of the dimensions of data through the graphical object. Visual characteristics and/or dimensions of data may additionally be subject to applied filters and/or constraints. When filters and/or constraints are applied resulting in any modification of the graphical object and/or dimensions of data, a subsequent data visual may be rendered. For some cases, a user interface may enable renderings of data visuals to occur dynamically upon modification of visual characteristics and/or dimensions of data.

Abstract: The composition of a data-driven analytics model that includes at least an analytical modeling component that defines analytical relationships between the model parameters using multiple analytical relations. The analytical modeling component uses the analytical relations to identify which of the model parameters are known and which are unknown, and solves for the identified unknown model parameter(s). The analytics modeling component also includes an analytics taxonomy in which the analytical relations are categorized into related analytics categories. Navigation through the analytics taxonomy assists in the composition of an analytics model. The analytics taxonomy may, but need not, be domain specific.

Abstract: Designing and executing a workflow having flow-based and constraint-based regions. A user selects one or more activities to be part of a constraint-based region. Each constraint-based region has a constraint associated therewith. The workflow is executed by executing the flow-based region and the constraint-based region. The flow-based region executes sequentially. The constraint is evaluated, and the constraint-based region executes responsive to the evaluated constraint.

Abstract: Searching and exploration using a data-driven analytics model. The analytics model includes an analytical modeling component that defines analytical relationships between model variables using a number of analytical relations. In response to a search request, the output variable(s) of the solve operation are identified. The output variable(s) may have even been identified based on the search request. The analytical relations of the model may then be used to solve for the identified output variable(s). The resulting value(s) for the now solved-for output variable(s) may then be used to formulate the response to the search request. The nature of the response may vary depending on the scope of the application that embodied the search request capability. The results of the search request may be used for further exploration of the model by, for example, submitting follow-up search requests, resulting in follow-up solve operations.

Abstract: A taxonomy reference model for use in an analytical modeling component. The analytical modeling component defines analytical relationships between the model parameters using analytical relations. The analytical modeling component uses the analytical relations to identify which of the model parameters are known and which are unknown, and solves for the identified unknown model parameter(s). An analytics taxonomy categorizes the analytics relations into analytics categories. This analytics taxonomy may be domain-specific. Also, if the analytics drive visuals, the visuals could be taxonomized as well. If the analytics is driven by data, the data could also be taxonomized.

Abstract: Designing and executing a workflow having flow-based and constraint-based regions. A user selects one or more activities to be part of a constraint-based region. Each constraint-based region has a constraint associated therewith. The workflow is executed by executing the flow-based region and the constraint-based region. The flow-based region executes sequentially. The constraint is evaluated, and the constraint-based region executes responsive to the evaluated constraint.

Abstract: A method for displaying complex relationships among data that includes selecting a first subset, a second subject, and a third subset of data from a database. Two or more relationships are identified among the first, second, and third subsets of data. One or more visualizations are provided that display the two or more relationships among the first, second, and third subsets of data.

Abstract: Visualization frameworks may include solvers. The solvers may be used to determine the properties of view components of view compositions. In some instances, the solvers may be explicitly composed using a relational structure, such as a dependency tree. In some instances, the solvers may be implicitly composed based on property-setters having solvers invoking other property-setters having solvers.

Abstract: The use of visual cues associated with rendered visual items to cue a user on whether a rendered visual item has interactive capability and/or what type of interaction is possible with that visual item. The visual items may be rendered in a data driven way with each constructed using a corresponding parameterized view component. The parameter(s) are populated by data, perhaps by model variables obtained from an analytical model. The parameters then drive logic associated with the view component to thereby construct a visual item which may then be rendered. The rendering engine then renders the visual item with the visual cue. The user may then interact with the rendered visual item. Such interaction might cause some external action to occur, might change which visual items are displayed, and/or might change a value of the input parameters of one or more view components used to generate displayed visual items.

Abstract: A renderable geometry the may be populated with shapes and data series. The geometry defines a set of dimensions to be applied to the shapes. The geometry further defines and enforces a construction of the shapes around the set of dimensions, and applies the data series to the plurality of shapes against at least one of the set of dimensions. The formulate of the geometry may be recursively performed. For instance, the geometry may be provided as a shape in a set of shapes provided to yet another geometry with another data series. The data series might be reversibly applied to one of the dimensions, allowing for complex geometries to be created with data represented in very flexibly ways.

Abstract: The application of chart data to a virtual space. The chart data is accessed and a virtual space is formulated. The virtual space is a computerized representation of a plurality of spatially interrelated visual items. The chart data is merged with the virtual space. As an example, the chart data might represent spatially significant information. For instance, one data item in the chart data might represent information about a first point or region in the virtual space, another data item in the chart data might represent information about a second point or region in the virtual space, and so forth. Merging of the chart data might involve changing property(s) of corresponding regions of space depending on value(s) of the associated chart data.

Abstract: The rendering of sequential data-driven scenes. Each data-driven scene is constructed using a plurality of view components, each receiving data into its input parameters, and using construction logic to formulate a rendering of corresponding visual item(s). When a transition even is detected, the data-driven scene changes from one scene to the next. For instance, the transition might occur by changing any one or more of the following: changing the data that is applied to the view components, 2) changing the set of view components, 3) changing the dimension set, or 4) changing one or more geometries used to construct the scene. Thus, data-driven scenes may be presented sequentially.

Abstract: The rendering on a user interface of a potentially complex computerized scene generation system. The user interface includes visual item(s) that have associated data. In addition, another set of visual items may be driven by data provided to input parameters, and may represent elements in the scene. Through user gestures, a user may correlate data items in the data source visual items with the element visual items to thereby automatically populate the element visual items with data, affecting the rendering of the data-driven element visual items. The element visual items might be linked, once again, perhaps through user gestures, to a parent visual item. In so doing, properties of the parent visual item might change and/or input parameters of the element visual items might change. Accordingly, complex visual scenes may be created through potentially quite simple user gestures.

Abstract: Designing and executing a workflow having flow-based and constraint-based regions. A user selects one or more activities to be part of a constraint-based region. Each constraint-based region has a constraint associated therewith. The workflow is executed by executing the flow-based region and the constraint-based region. The flow-based region executes sequentially. The constraint is evaluated, and the constraint-based region executes responsive to the evaluated constraint.

Abstract: Designing and executing a workflow having flow-based and constraint-based regions. A user selects one or more activities to be part of a constraint-based region. Each constraint-based region has a constraint associated therewith. The workflow is executed by executing the flow-based region and the constraint-based region. The flow-based region executes sequentially. The constraint is evaluated, and the constraint-based region executes responsive to the evaluated constraint.

Abstract: The composition of a data-driven analytics model that includes at least an analytical modeling component that defines analytical relationships between the model parameters using multiple analytical relations. The analytical modeling component uses the analytical relations to identify which of the model parameters are known and which are unknown, and solves for the identified unknown model parameter(s). The analytics modeling component also includes an analytics taxonomy in which the analytical relations are categorized into related analytics categories. Navigation through the analytics taxonomy assists in the composition of an analytics model. The analytics taxonomy may, but need not, be domain specific.

Abstract: Searching and exploration using a data-driven analytics model. The analytics model includes an analytical modeling component that defines analytical relationships between model variables using a number of analytical relations. In response to a search request, the output variable(s) of the solve operation are identified. The output variable(s) may have even been identified based on the search request. The analytical relations of the model may then be used to solve for the identified output variable(s). The resulting value(s) for the now solved-for output variable(s) may then be used to formulate the response to the search request. The nature of the response may vary depending on the scope of the application that embodied the search request capability. The results of the search request may be used for further exploration of the model by, for example, submitting follow-up search requests, resulting in follow-up solve operations.