Abstract: A computing device displays a data visualization in a graphical user interface. The device receives user input to specify a natural language command related to the data visualization. The device determines that the natural language command includes a metacommand to modify the data visualization by: (1) adding a new data field, or (2) removing one of the one or more first data fields, or (3) replacing one of the one or more first data fields with another data field. The device determines one or more proposed actions in accordance with the metacommand and ranks the proposed actions based on the saliency or weight of a missing term that is inferred from the natural language command. The device receives user selection of a first proposed action. In response to the user selection, the device generates and displays an updated data visualization.

Abstract: At a computer system a graphical user interface is displayed on a display. The graphical user interface includes (a) a schema region displaying data field icons, each data field icon representing a respective data field from a user-selected data source, and (b) a canvas region. In response to receiving user input selecting a data field icon from the schema region and placing it on the canvas region, a block card is displayed on the canvas region. The block card includes user interface affordances to designate (i) a layout type that specifies a row-based arrangement, a column-based arrangement, or an inline arrangement, (ii) a mark type that specifies a shape for visual data marks, and (iii) one or more visual encodings. A data visualization for the data source is generated and displayed according to the layout type, the mark type, and the visual encodings specified on the block card.

Abstract: A method uses natural language for visual analysis of a dataset, and is performed at a computer having a display, processors, and memory. The computer displays a data visualization based on a dataset retrieved from a database and receives a first user input to specify a natural language command related to the displayed data visualization. Based on the displayed data visualization and the natural language command, the computer identifies an initial range of values for a first data field in the dataset and displays an editable user interface control showing the initial range of values. The computer receives a second user input, at the user interface control, specifying a modified range of values for the first data field and retrieves an updated dataset from the database using the modified range of values for the first data field. The computer then updates the displayed data visualization using the updated dataset.

Abstract: A method utilizes data discovery to support visual analysis of datasets. A user selects a data source, and the method presents a natural language interface for analysis of the data source. A user specifies an incomplete natural language command directed to the data source, and the method correlates words in the incomplete natural language command with a data field in the data source. The method determines the data type of the data field and a range of data values for the data field. According to the data type and the range of data values, the method presents one or more autocompletion options for the incomplete natural language command. Each option includes respective text and a respective corresponding visual graphic. The user selects one of the autocompletion options, and the method forms a complete natural language command. The method then displays a data visualization according to the complete natural language command.

Abstract: A computer system receives a natural language input directed to a data source. In response to receiving the natural language input, and in accordance with a determination that the natural language input specifies an incomplete natural language command directed to the data source, the computer system presents a map widget for selecting geospatial data points from the data source. The map widget includes a map having a plurality of predetermined geographic regions. The computer system receives user specification of a user-defined region in the map included in the map widget. In accordance with receiving the user specification and based on a coverage metric computed for the plurality of geographic regions, the computer system selects one or more geographic regions. The computer system completes the natural language command with the selected geographic regions. The computer system generates and displays a map data visualization according to the completed natural language command.

Abstract: A method generates data visualizations. A device receives a first user input to specify a natural language command directed to a data source. The natural language command includes a first keyword that is a gradable adjective that modifies a second keyword in the natural language command. The device identifies a numerical data field, from the data source, whose field name co-occurs with the first keyword in natural language utterances in a predefined stored corpus of natural language utterances, based on quantifying co-occurrence using a co-occurrence metric. The device computes a numerical range for values of the numerical data field based on correlation of sentiment polarity of the first keyword and the field name of the numerical data field. The device then generates a data visualization for the data source, based on filtering rows of data from the data source according to the numerical range for the numerical data field.

Abstract: A computing device displays, in a graphical user interface, a data visualization and a plurality of phrases that collectively define the data visualization. The device receives user input to specify a natural language command related to the data visualization. The device determines that the natural language command includes a metacommand to: (1) add a data field to the data visualization, or (2) remove a data field from the data visualization, or (3) replace a data field in the data visualization with another data field. The device determines one or more proposed actions in accordance with the determined metacommand and ranks the proposed actions. The device receives user selection of a first proposed action. In response to the user selection, the device generates and displays an updated data visualization and one or more updated phrases that collectively define the updated data visualization.

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 request for information from the data source. The computing device extracts one or more first keywords and determines, based on the first 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 does not directly specify a characteristic in the initial visual specification. The computing device generates a modified visual specification. The computing device generates one or more queries based on the modified visual specification. The computing device executes the one or more 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. The method is performed at a computer system. 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 data visualization within the graphical user interface for selecting geospatial data points from the data source. In response to receiving a first user input to select a first set of one or more geographic regions, the system calculates the similarity between the first set of one or more geographic regions and a second set of one or more geographic regions, based on a range of socio-economic, demographic, and/or geographic data fields from the data source, using one or more statistical techniques (e.g., Jensen-Shannon Divergence (JSD)). The system then updates and displays the map data visualization according to the calculated similarity.

Abstract: A computing device receives a user input to specify a natural language command directed to a data source. The device identifies a first keyword, corresponding to a first data field from the data source, in the natural language command. The device identifies a second keyword in the natural language command. The second keyword comprises an adjective that provides context for the first data field. The device generates a visual specification based on the first and second keywords. The visual specification specifies the data source, a plurality of visual variables, and a plurality of data fields from the data source, including the first data field. The visual variables include a first visual variable that specifies filtering of the first data field according to the second keyword. The device generates and displays a data visualization based on the visual specification.

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: Embodiments are directed to intent driven dashboard recommendations. A plurality of collection specifications that declare visualization declarations may be provided such that each collection specification is associated with an author intent. And attributes-of-interest selected from a data source may be provided. A plurality of collections may be generated based on the plurality of collection specifications, the attributes-of-interest, or data from the data source such that each collection may include visualizations that may be based on the visualization declarations. A preference score may be generated for each collection based on the author intent, visualizations in each collection, or the attributes-of-interest. The plurality of collections may be classified based on the preference scores associated with the classified collections such that the classified collections may be displayed based on the preference scores.

Abstract: A computing device receives user input to specify a natural language command directed to a data source. In accordance with the user input, the device forms an intermediate expression according to a context-free grammar and a semantic model of data fields in the data source. The natural language command includes (i) a first term that specifies an aggregation type in a first aggregation, (ii) a second term that specifies a data field, in the semantic model, to be aggregated for the first aggregation, and (iii) terms that specify data fields, in the semantic model, to determine grouping for the first aggregation. The device translates the intermediate expression into database queries, executes the database queries to retrieve one or more data sets from the data source, aggregated according to the first aggregation, then generates and displays a data visualization of the retrieved data sets.

Abstract: A method provides visual analysis of datasets. The method is performed at a computer system. 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 data visualization within the graphical user interface for selecting geospatial data points from the data source. In response to receiving a first user input to select a first set of one or more geographic regions, the system calculates the similarity between the first set of one or more geographic regions and a second set of one or more geographic regions, based on a range of socio-economic, demographic, and/or geographic data fields from the data source, using one or more statistical techniques (e.g., Jensen-Shannon Divergence (JSD)). The system then updates and displays the map data visualization according to the calculated similarity.

Abstract: An electronic device has a first device type. 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. The device formulates, for each of the intent types, a respective intent specification according to the request. In some implementations, the device determines respective properties of the respective intent specification according to the respective predefined attributes and according to data fields, from the data source, specified in the request. The device generates, for each of the intent types, a respective output specification that specifies a respective recommended data visualization type. The device renders data analysis responsive to the request in accordance with the electronic device having the first device type.

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: 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.

Abstract: A method executes at a computing device that includes a display, one or more processors, and memory. The method includes receiving user input to specify a data source. The method includes receiving a first user input in a first region of a graphical user interface to specify a natural language command related to the data source. The device determines, based on the first user input, that the natural language command includes a table calculation expression. In accordance with the determination, the method identifies a second data field in the data source, Values of the first data field are aggregated for each of the time periods in a range of dates according to the second data field. A respective difference between the aggregated values for each consecutive pair of time periods is computed. A data visualization is generated and displayed.