Abstract: A computing device displays an initial data visualization according to an initial visual specification that specifies a data source, visual variables, and data fields from the data source. The computing device receives a natural language command that includes a user request for information from the data source. The computing device identifies one or more keywords from the natural language command and determines, based on the keywords and one or more of (i) the data source, (ii) the visual variables, and/or (iii) the data fields of the initial visual specification, that the request indirectly specifies a first visual characteristic in the initial visual specification. The computing device generates one or more queries in accordance with the determining. The computing device executes the queries to retrieve data for a modified data visualization. The computing device generates and displays the modified data visualization.

Abstract: A method provides visual analysis of datasets. A user selects a data source. In response, the system presents a natural language interface for analysis of data in the data source and presents a map widget for selecting geospatial data points from the data source. A user provides a first user input to specify an incomplete natural language command directed to the data source and provides a second user input to select a region in the map widget. In response to the first user input and the second user input, the system generates and displays a map data visualization according to the first user input and the second user input, including a linguistic description of data points in the selected region. The system continues to display the selected region in the map widget. The map widget is configured to update the data visualization according to user modification of the selected region.

Abstract: A method generates map visualizations with multiple map layers. A user selects a data source with geographic data. A device displays a data visualization user interface, including a schema information region with data fields, and shelf regions that defining characteristics for a data visualization. The user selects a first geographic data, and the user interface generates a map data visualization using coordinates associated with the first geographic data field. The visualization includes a first plurality of data marks in a first layer. The user selects a second geographic data field. In response, the user interface displays a new layer icon. Upon activation of the new layer icon by the second geographic data field, the user interface superimposes a second layer over the existing map data visualization to form an updated map data visualization. The second layer includes a second plurality of data marks corresponding to the second geographic data field.

Abstract: A computing device generates a first set of glyphs from a first data set. Each glyph in the first set of glyphs represents the same set of user-selected columns in a first row of the plurality of rows in the data set. For each glyph in the first set of glyphs, the computing device defines a set of marks to be displayed. Each mark is defined by one or more visual features, with a respective visual feature assigned to each column of the user-selected columns. Each visual feature is displayed with a visual property determined based on a data value in the first row for the respective column. A user specifies a specific visual feature for a first mark from the first set of glyphs. The computing device generates a second set of glyphs. Each glyph in the second set of glyphs includes a mark with the specific visual feature.

Abstract: A computing device receives user selection of a data source. The computing device receives a user input to specify a level of detail (LOD) expression, which includes a first keyword, a SORT keyword, and an analytic expression. In response to the user input, the computing device identifies one or more data fields from the data source. The device translates the LOD expression into one or more executable database queries referencing the identified data fields. The computing device executes the queries to retrieve data from the data source. The computing device generates and displays a data visualization using the retrieved data.

Abstract: A computing device receives from a user a partial natural language input related to a data source. The computing device receives an additional keystroke corresponding to the partial natural language input. The partial natural language input and the additional keystroke comprise a character string. In response to the additional keystroke, the computing device generates one or more interpretations corresponding to entities in the data source. The computing device displays the interpretations. In some implementation, the character string comprises a sequence of terms, and the device displays the interpretations in a dropdown menu adjacent to the most recently entered term in the sequence. In some implementations, the dropdown menu includes a plurality of rows, each row displaying a respective data value and a respective data field corresponding to the respective data value. Some implementations display a statistical distribution of data values for a data field (displayed adjacent to the first interpretation).

Abstract: The various implementations described herein include methods and devices for using a messaging system to analyze and distribute data analytics. In one aspect, a method includes displaying a user interface for a communications application, the user interface including a dashboard display area for a group of users. In response to a user input, display of the dashboard display area is replaced with a view corresponding to a data analysis application. The data analysis application is separate from the communications application, and the view corresponding to the data analysis application provides access to reports generated, using credentials of a user of the communications application, without user input to execute or open the data analysis application. A second user input is received requesting to share a report with the selected group of users, and in response, the report is published to the dashboard display area for the selected group of users.

Abstract: Embodiments are directed to managing a data model that includes data objects associated with layers of the data model may be provided. A lineage model that includes lineage objects that has one or more lineage objects associated with a layer may be provided. A lineage object associated with a layer may be activated. A first portion data objects may be determined based on the activated lineage object. An anchor object may be determined from the first portion of data objects. A search of the data model may be employed to provide dependency information based on a position of the anchor data object in the data model. A second portion of data objects may be determined based on the dependency information. A report that includes a list of the second portion of data objects, the dependency information, or the like, may be provided.

Abstract: Embodiments are directed to managing character encoding. A plurality characters that are each encoded as code units based on a character code may be provided such that the code units for each character represents a code point of a character encoding scheme. An encoding model may be determined based on the character code, one or more processor features, and a target character code. Process features may be employed to transform the code units into target code units based on the encoding model such that the target code units are based on the target character code and such that the target code units encode the code point for each character. The plurality of target characters may be provided to a target stream such that each target character may be encoded as the target code units.

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: A method builds a regression model for predicting processing times for federated queries using a variety of data sources. The method includes obtaining federated queries (e.g., from benchmarks), and generates a plurality of federated query plans for each federated query. Each federated query plan corresponds to executing a respective federated query using a respective data source as the federation engine. The method includes forming feature vectors for each federated query plan based on cost estimations for executing the respective federated query plan and cost estimations for data transfer. The method further includes training a regression model, using the feature vectors for the plurality of federated query plans, to predict runtimes for executing federated queries using the variety of data sources as a federation engine. Some implementations use the trained regression model to determine a suitable federation engine for a given federated query.

Abstract: A computing device displays a data visualization interface that includes a shelf region and a schema information region. The device receives user input to select a measure data field and a dimension data field from the schema information region. The device generates a custom calculation that groups data values of the dimension data field according to respective distinct data values of the dimension data field and aggregates data values of the measure data field for each of the distinct data values of the dimension data field. The device stores the custom calculation as a new selectable data field and displays the new selectable data field in the schema information region. The device receives user selection of the new selectable data field from the schema information region and placement of the new selectable data field in the shelf region. The device generates and displays a data visualization.

Abstract: Embodiments are directed to managing visualizations of data using a network computer. A modeling engine may provide a data model that includes a plurality of data objects and a display model that includes a plurality of display objects based on the plurality of data objects. Parameter action objects may be associated with display objects in the display model. In response to an activation of the parameter action objects, values associated with display objects may be assigned to the parameter associated with a parameter action object and actions associated with the parameter may be executed to modify the display model.

Abstract: A computing device generates an initial data visualization according to a visual specification that specifies a data source, a plurality of visual variables, and a plurality of data fields from the data source. The device determines a visualization region based on a display size of the initial data visualization. The device generates a modified data visualization from the initial data visualization according to the determined visualization region, including: (i) identifying a plurality of elements of the initial data visualization, (ii) for each element of the plurality of identified elements, determining a respective semantic weight for the respective element, (iii) grouping the elements into one or more element layers according to the determined semantic weights, (iv) computing spatial metrics for the elements, and (v) executing one or more generalization operations on the elements in accordance with the one or more computed spatial metrics. The computing device displays the modified data visualization.

Abstract: A method for analyzing data from data sources includes receiving user selection of a data source and displaying an interface that includes a schema region and a plurality of shelf regions. Each data field in the schema region is associated with a respective system-defined object from the data source. In a first display mode, each data field is displayed as associated with a respective system-defined object. In a second display mode, each data field is displayed in a respective user-defined folder. The method also includes displaying a data visualization according to a user placement of first and second data fields into the first and second shelf regions.

Abstract: A computer generates and displays a data visualization in a data visualization user interface according to placement of data fields, from a data source. The data visualization includes visual data marks representing data from the data source. The computer detects a user input to select a visual data mark. In response to detecting the user input, the computer obtains a data model encoding the data source as a tree of logical tables. The computer identifies one or more aggregated data values for the visual data mark, each of the aggregated data values corresponding to a respective data field in the data model. For each of the aggregated data values, the computer retrieves a respective disaggregated set of data rows from a respective logical table containing the respective data field. The computer displays a summary grid, with a respective tab corresponding to each of the retrieved disaggregated sets of data rows.

Abstract: Embodiments are directed to managing data visualizations. Candidate data fields from a data source may be determined based on a search expression. The candidate data fields may be displayed in the model panel. A working data model may be generated based on a portion of the candidate data fields such that the portion of the candidate data fields may be included in the working data model. Visualizations may be determined based on recommendation models and the working data model such that a portion of the visualizations may be determined based on shared data fields that may be included in the working data model and the visualizations. A working visualization may be determined based on a visualization listed in the display panel and the working data model such that data fields included in the working data model may be associated with the working visualization.

Abstract: Embodiments are directed to managing user interfaces. User interface (UI) models associated with concepts may be provided such that the UI models include visualizations. Other concepts may be associated with a data model based on fields of the data model. Characteristics of the concepts associated with each UI model and the other concepts associated with the data model may be compared to each other such that results of each comparison may be employed to generate a score for each UI model. The UI models may be ordered based on each score. A report that includes a rank ordered list of the UI models may be provided.

Abstract: An electronic device has one or more processors and memory. The memory stores one or more programs configured for execution by the one or more processors. The device receives a request directed to a data source. The request includes one or more intent types and one or more predefined attributes associated with the intent types. Each of the predefined attributes limits a respective data analysis operation of a respective intent type. In response to the request, for each of the intent types, the device formulates a respective intent specification according to the request. The device also applies one or more respective rules corresponding to the respective intent type to determine whether the respective intent type is ambiguous and/or underspecified. When the respective intent type is ambiguous and/or underspecified, the device infers information to resolve the ambiguous and/or underspecified intent.

Abstract: An electronic device running a first application has one or more processors and memory. The memory stores one or more programs configured for execution by the one or more processors. The device downloads, from a server system, a visual analytics intent library, which is configured to execute across a plurality of applications, including the first application. The device configures the library to execute on the device based on the device running the first application. After the configuring, the device receives a request directed to a data source. The request includes one or more intent types and one or more predefined attributes associated with the one or more intent types. In accordance with the request, for each of the intent types, the device formulates a respective intent specification according to the library. It also determines one or more respective properties of the respective intent specification according to the respective predefined attributes.