Abstract: A user selects a data source. The user also selects a first point in time and a second point in time. The visualization platform generates a query to perform a full outer join between (1) a first data set corresponding to the first point in time and (2) a second data set corresponding to the second point in time. The platform then executes the query to retrieve a result set. The platform classifies each record in the retrieved result set as new, present, or removed according to whether the respective record: (1) corresponds only to a record in the second data set; (2) corresponds to records in both the first data set and the second data set; or (3) corresponds only to a record in the first data set. Finally, the platform generates and displays a Sankey diagram or Waterfall chart according to the retrieved result set and the classifications.

Abstract: Embodiments are directed to a display engine that provides a graphical user interface (GUI) that includes a graph panel and a visualization panel may be generated. The graph panel may receive graph related inputs or interactions. Graph information based on the graph related inputs or interactions and a data source may be generated. A graph model based on the graph information and the data source may be generated. Elements that correspond to objects in the graph model or relationships in the graph model may be displayed in the graph panel. The graph information and the graph model may be employed to execute a query that provides a data model based on results of the query. The data model may be employed to provide a visualization that is displayed in the display panel.

Abstract: Embodiments are directed to managing visualizations of data. Visualizations to monitor may be determined based on metrics associated with the monitored visualizations. Mark values may be sampled from each monitored visualization such that the sampled mark values may be stored with a timestamp. If an amount of the sampled mark values for a monitored visualization exceeds a threshold value mark models may be trained to classify a portion of the sampled mark values associated with the monitored visualization such that the mark models may predict ranges of values for the classified mark values associated with the monitored visualization. If the portion of the classified mark values associated with the monitored visualization has a value outside of the predicted range values, indicating that the monitored visualization may be associated with an anomalous mark value.

Abstract: Embodiments are directed to managing visualizations of data. A primary data set may be provided based on a data model and a query that includes a first time point and a second time point. A first data set and second data set that correspond to versions of the primary data set that correspond to the first time point and the second time point. A result data set may be generated based on the first data set and the second data set. The result data set may be classified based on the first data set and the second data set such that each record in the result data set may be classified as new, present, or removed. Changes to the primary data set that occur between the first time point and the second time point may be determined based on the classified records. Visualizations that represent the changes may be generated.

Abstract: Embodiments are directed to managing visualizations of data. A provided data model may include a tree specification that declares parent-child relationships between objects in the data model. In response to a query associated with objects in the data model: employing the parent-child relationships to determine a tree that includes parent objects and child objects from the objects based on the parent-child relationships; determining a root object based on the query and the tree; traversing the tree from the root object to visit the child objects in the tree; determining partial results based on characteristics of the visited child objects such that the partial results are stored in an intermediate table; and providing a response to the query that includes values based on the intermediate table and the partial results.

Abstract: Embodiments are directed to generating metrics based on visualizations. A dashboard that may be associated with source visualizations display a current value of metrics from source visualization models. A classifier may automatically use characteristics from the source visualizations to determine metrics for the source visualization. The source visualization models may be sample to provide values of the metrics across time, at a sampling rate determined by a metric profile. The sampled values may be stored with time values in a metric data store such that the time values may correspond to when the values sampled from the visualization. Metric visualizations may be generated based on the values and the time values such that the metric visualizations display previously sampled values of the metrics.

Abstract: Embodiments are directed to visualizing data. A graphical user interface (GUI) that includes a shelf panel and a display panel may be generated. Column fields may be generated and displayed in the shelf panel based on a data source or a table specification. The table specification may be employed to generate a table view that may be displayed in the display panel such that the table view includes columns that correspond to each column field. Data items may be determined based on the data source and the column fields such that each column field corresponds to a portion of the data items. Each portion of the data items may be displayed in a column of the table view that corresponds to the column field that corresponds to the portion of the data items.

Abstract: Embodiments are directed to generating metrics based on visualizations. A dashboard that may be associated with source visualizations display a current value of metrics from source visualization models. A classifier may automatically use characteristics from the source visualizations to determine metrics for the source visualization. The source visualization models may be sample to provide values of the metrics across time, at a sampling rate determined by a metric profile. The sampled values may be stored with time values in a metric data store such that the time values may correspond to when the values sampled from the visualization. Metric visualizations may be generated based on the values and the time values such that the metric visualizations display previously sampled values of the metrics.

Abstract: Embodiments are directed to visualizing data. A graphical user interface (GUI) that includes a shelf panel and a display panel may be generated. Column fields may be generated and displayed in the shelf panel based on a data source or a table specification. The table specification may be employed to generate a table view that may be displayed in the display panel such that the table view includes columns that correspond to each column field. Data items may be determined based on the data source and the column fields such that each column field corresponds to a portion of the data items. Each portion of the data items may be displayed in a column of the table view that corresponds to the column field that corresponds to the portion of the data items.

Abstract: Embodiments are directed to a display engine that provides a graphical user interface (GUI) that includes a graph panel and a visualization panel may be generated. The graph panel may receive graph related inputs or interactions. Graph information based on the graph related inputs or interactions and a data source may be generated. A graph model based on the graph information and the data source may be generated. Elements that correspond to objects in the graph model or relationships in the graph model may be displayed in the graph panel. The graph information and the graph model may be employed to execute a query that provides a data model based on results of the query. The data model may be employed to provide a visualization that is displayed in the display panel.

Abstract: An apparatus and method define a fit of a set of upper and lower teeth of a patient by generating a computer representation of the teeth; and determining an occlusion from the computer representation of the teeth using one or more keys.

Abstract: An apparatus and method define a fit of a set of upper and lower teeth of a patient by generating a computer representation of the teeth; and determining an occlusion from the computer representation of the teeth using one or more keys.

Abstract: An apparatus and method define a fit of a set of upper and lower teeth of a patient by generating a computer representation of the teeth; and determining an occlusion from the computer representation of the teeth using one or more keys.

Abstract: An apparatus and method define a fit of a set of upper and lower teeth of a patient by generating a computer representation of the teeth; and determining an occlusion from the computer representation of the teeth using one or more keys.

Abstract: A method of generating a statistical model for a dataset operates at a computer system having one or more processors and memory. The memory stores one or more programs configured for execution by the one or more processors. The process receives a visual specification. The visual specification defines a graphical representation of a portion of the dataset. The visual specification includes a first field and a second field of the dataset. The method determines a set of data properties for each of the first and second fields. The process then generates a statistical model of a mathematical relationship between the first and second fields based on the data properties of the first and second fields and data values associated with the first and second fields in the dataset. The process displays the graphical representation and the statistical model superimposed on the graphical representation.

Abstract: A method of generating a statistical model for a dataset operates at a computer system having one or more processors and memory. The memory stores one or more programs configured for execution by the one or more processors. The process receives a visual specification. The visual specification defines a graphical representation of a portion of the dataset. The visual specification includes a first field and a second field of the dataset. The method determines a set of data properties for each of the first and second fields. The process then generates a statistical model of a mathematical relationship between the first and second fields based on the data properties of the first and second fields and data values associated with the first and second fields in the dataset. The process displays the graphical representation and the statistical model superimposed on the graphical representation.

Abstract: A method of automatically generating models from a dataset includes multiple steps. First, a description of a view of a dataset is provided. The description includes multiple fields associated with the dataset. Next, a set of properties is determined for each of the multiple fields. Finally, the description is automatically translated into one or more models based on the respective properties of the multiple fields and a set of predefined heuristics.

Abstract: An apparatus and method define a fit of a set of upper and lower teeth of a patient by generating a computer representation of the teeth; and determining an occlusion from the computer representation of the teeth using one or more keys.

Abstract: An apparatus and method define a fit of a set of upper and lower teeth of a patient by generating a computer representation of the teeth; and determining an occlusion from the computer representation of the teeth using one or more keys.

Abstract: A method of automatically generating models from a dataset includes multiple steps. First, a description of a view of a dataset is provided. The description includes multiple fields associated with the dataset. Next, a set of properties is determined for each of the multiple fields. Finally, the description is automatically translated into one or more models based on the respective properties of the multiple fields and a set of predefined heuristics.