Abstract: The subject disclosure is directed towards a web service that maintains a set of models used to generate plans, such as vacation plans, in which the set of models includes models that are authored by crowd contributors via the service. The models include rules, constraints and/or equations, and may be text based and declarative such that any author can edit an existing model or combination of existing models into a new model. Users can access the models to generate a plan according to user parameters, view a presentation of that plan, and interact to provide new parameters to the model and/or with objects in the plan to modify the plan and view a presentation of the modified plan.

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: 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.

Abstract: A solver framework for use with an analytical model. The analytical model includes multiple model parameters and includes definitions for analytical relationships between the model parameters. The solver framework coordinates the processing of multiple specialized solvers. In particular, the solver framework identifies which model parameters are input model variables and which are output model variables. The solver framework then analyzes dependencies to determine a solve order to solve for the output model variables. The solver framework then charged the specialized solvers with performing portions of the solve operation such that the specialized solvers solve for the output model variables in an order which considers the dependencies. In one embodiment, additional or replacement solvers may register with the solver framework to thereby make the specialized solver available for solving for output model variables in the future.

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: A system, method, and apparatus for annotating an electronic document independently of its content is provided. According to one variation, a tablet and stylus-based computer is programmed with a document browser that permits a user to annotate documents viewed through the browser. The annotations are stored separately from the viewed document pages but are correlated with the pages such that when a previously annotated page is revisited, annotations relating to that page are retrieved and displayed on top of the page as an “ink” layer. Three different annotation modes are possible: ink, highlight, and erase. Each mode can be selected through a user interface supplied through the browser or through controls embedded in a document.

Abstract: A system, method, and apparatus for annotating an electronic document independently of its content is provided. According to one variation, a tablet and stylus-based computer is programmed with a document browser that permits a user to annotate documents viewed through the browser. The annotations are stored separately from the viewed document pages but are correlated with the pages such that when a previously annotated page is revisited, annotations relating to that page are retrieved and displayed on top of the page as an “ink” layer. Three different annotation modes are possible: ink, highlight, and erase. Each mode can be selected through a user interface supplied through the browser or through controls embedded in a document.

Abstract: The inference of a dependency graph that represents a graph of solves that leads from input model parameter(s) to output model parameters using analytics. In one embodiment, the dependency graph is part of visually driven analytics in which the output model parameter(s) are used to formulate data-drive scenes. As the identity of the input and/or output model parameter(s) change, or as the analytics themselves change, the dependency graph may also change. This might trigger a resolve of the analytics. In one embodiment, the intermediate parameters involved in the dependency graph may be viewed and evaluated by the user.

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 use of a data stream object to enumerate elements of a data stream to thereby drive rendering of a data-driven model. The data driven model includes multiple view components that may use their own construction logic to render visual items based on data provided to their input parameter(s). The data stream may be quite large, in which case, only a portion of the data stream is enumerated by the data stream object. The enumerated elements of the data stream may be used to populate the input parameters of the view components, and or may be provided to analytics, from which input parameters of the view components may be derived. Thus, a data stream, regardless of its size, may be dealt with in the consistent manner to thereby drive the data-driven model.

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: 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: Visual items may each be constructed and placed in position using logic defined by a view component corresponding to each visual item, where that logic may depend on one or more values populated into parameter(s) of the view component. Some of those parameter values may correspond to known model parameter values. Others, however, may have been solved for using a model that defines analytical relationships between the model parameters. In one embodiment, which of the model parameters are known, and which are unknown, may not be predetermined. Accordingly, a solver might be prepared for multiple solve operation paths even using a single model. The view composition process may be entirely data-driven, with the solve and/or the visual items implemented using spreadsheets.

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 use of a data structure that is a symbolic representation of a keyed array that has an array variable and an associated key variable. There is a correlation maintained between the variable type of the array variable and the corresponding keying set that is to be bound to the associated key variable. The keyed array may remain unbound thereby being simply symbolically represented, or the keying set may be bound to the key variable more immediately. In one embodiment, once the keying set is bound to the key variable, data may be bound to the array variable itself. This may be repeated for multiple keyed arrays. The data from multiple keyed arrays may be operated upon to about another array of values, which may then be aggregated in some way.

Abstract: Solving for output variable(s) of a model that includes multiple analytically related model variables. The identity of the output model variables and the analytical relationships between the model variables are separately designated. Regardless of the identity of the output variable(s), a solver framework interprets the analytical relationships and solves for the designated output variable(s). The output model variable(s) may be designated separately than the analytical relationships themselves. By simply changing the designation of the output model variable(s), the analytical relationships are reevaluated, and the output variable(s) are solved for. The solver framework itself stays the same regardless of the identity of the output model variable(s).

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.