Abstract: Embodiments of the present disclosure pertain to network based machine learning generated simulations. In one embodiment, the present disclosure includes a computer implemented method comprising sending first code comprising a programmable calculator from a server system to a client system across a network. A data request is sent to a database, the data request configured to retrieve data from the database comprising a plurality of fields and a target field. The retrieved data is processed using a machine learning algorithm to produce a weight for each field of the plurality of fields and a scoring data structure. The fields and the scoring data structure are sent to the client system across the network. A user selects values for the plurality of fields and the programmable calculator is configured based on the scoring data structure to generate a simulated value for the target field based on the user selected values.

Abstract: A system, method, and computer-readable medium to receive a request to render a visualization, the visualization to include a plurality of data point representations; determine an optimal number of data point representations to include on a first page of the visualization; render an active range of the plurality of data point representations on the first page of the visualization; apply at least one post-rendering optimization to the rendered first page; determine an estimation of an application of the at least one post-rendering optimization to a next page of the visualization; store the estimation of the application of at the least one post-rendering optimization to the next page of the visualization in a buffer memory; and display the rendered first page having the at least one post-rendering optimization applied thereto.

Abstract: Embodiments of the present disclosure pertain to network based machine learning generated simulations. In one embodiment, the present disclosure includes a computer implemented method comprising sending first code comprising a programmable calculator from a server system to a client system across a network. A data request is sent to a database, the data request configured to retrieve data from the database comprising a plurality of fields and a target field. The retrieved data is processed using a machine learning algorithm to produce a weight for each field of the plurality of fields and a scoring data structure. The fields and the scoring data structure are sent to the client system across the network. A user selects values for the plurality of fields and the programmable calculator is configured based on the scoring data structure to generate a simulated value for the target field based on the user selected values.

Abstract: Embodiments automatically create an axis break in a bar chart or waterfall chart to promote data visualization. For a bar chart with positive bars, a maximum axis break point is determined relative to a lowest datapoint (D1) in the selected bar(s). A minimum axis break point is found from a value (D2) outside selection which is between D1 and zero, and which is closest to D1. The inclusion of padding on either side of a break prevents the break from lying at zero or a bar end. A different procedure creates axis breaks in a waterfall chart. Certain embodiments store only bar selection metadata with a chart. The location of the axis break then is recalculated each time the chart is rendered (e.g., with updated data). Embodiments may retain axis break data for reference in various stages of a user interaction (e.g., filtering/expanding, drilling-down/up, etc.) with a rendered chart.

Abstract: A system, method, and computer-readable medium to receive a request to render a visualization, the visualization to include a plurality of data point representations; determine an optimal number of data point representations to include on a first page of the visualization; render an active range of the plurality of data point representations on the first page of the visualization; apply at least one post-rendering optimization to the rendered first page; determine an estimation of an application of the at least one post-rendering optimization to a next page of the visualization; store the estimation of the application of at the least one post-rendering optimization to the next page of the visualization in a buffer memory; and display the rendered first page having the at least one post-rendering optimization applied thereto.

Abstract: Embodiments automatically create an axis break in a bar chart or waterfall chart to promote data visualization. For a bar chart with positive bars, a maximum axis break point is determined relative to a lowest datapoint (D1) in the selected bar(s). A minimum axis break point is found from a value (D2) outside selection which is between Dl and zero, and which is closest to D1. The inclusion of padding on either side of a break prevents the break from lying at zero or a bar end. A different procedure creates axis breaks in a waterfall chart. Certain embodiments store only bar selection metadata with a chart. The location of the axis break then is recalculated each time the chart is rendered (e.g., with updated data). Embodiments may retain axis break data for reference in various stages of a user interaction (e.g., filtering/expanding, drilling-down/up, etc.) with a rendered chart.

Abstract: Embodiments link external widgets to internal data displayed being within a dashboard as part of a story. An interface allows selecting external widget(s) based upon matching with a parameter of internal data being accessed. A user may define in an interactive manner, the behavior of the external widget in terms of characteristics such as data rendering, configurable fields, update behavior, and/or access to external sources (e.g., authentication). In an example, internal geographic dashboard data may serve as a basis for accessing external data such as social media data via a custom widget searching relevant messages combined with keywords (e.g., referring to geography). An external widget may be linked to other (internal/external) widgets through a linking table, such that changed/updated data is propagated for display within the dashboard. External widgets may thus be linked to data currently being viewed in terms of chart selection, data highlighting and data filtering mechanisms.

Abstract: Embodiments link external widgets to internal data displayed being within a dashboard as part of a story. An interface allows selecting external widget(s) based upon matching with a parameter of internal data being accessed. A user may define in an interactive manner, the behavior of the external widget in terms of characteristics such as data rendering, configurable fields, update behavior, and/or access to external sources (e.g., authentication). In an example, internal geographic dashboard data may serve as a basis for accessing external data such as social media data via a custom widget searching relevant messages combined with keywords (e.g., referring to geography). An external widget may be linked to other (internal/external) widgets through a linking table, such that changed/updated data is propagated for display within the dashboard. External widgets may thus be linked to data currently being viewed in terms of chart selection, data highlighting and data filtering mechanisms.

Abstract: Data is received by a content management system that identifies a first document managed by the content management system. Thereafter, the first document is associated with a first user that authored or edited the first document. Subsequently, the first user is associated with at least one chained document different from the first document that has at least one pre-defined attribute associated with the first user. Data can then be provided that characterizes the at least one chained document. Related apparatus, systems, techniques and articles are also described.

Abstract: A computer readable storage medium includes executable instructions to open a report including a view time interaction module, to display structured report data provided to the view time interaction module, and to modify the structured report data in response to a call to the report initiated by the view time interaction module, where the call is based on update information generated by the view time interaction module in response to user input.