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 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 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: At a computer system a graphical user interface is displayed that includes a schema region displaying data field icons, each data field icon representing a respective data field, and a canvas region including block cards. Each block card specifies a respective layout type that is one of: a row-based arrangement, a column-based arrangement, or an inline arrangement. Each block card is generated in response to user placement of one or more respective data field icons onto the canvas region. For each block card, a respective database query is generated and executed to retrieve tuples of data for data fields associated with the respective block card. For each retrieved tuple, a respective two-dimensional grid position, including a respective row index and a respective column index, is computed. A data visualization is generated and displayed. Each retrieved tuple is represented as a visual mark displayed at its respective computed grid position.

Abstract: At a computer system a number of interrelated block object specifications are generated according to user placement of data field icons on a canvas region. Each block object specification includes a layout type, a mark type, and one or more visual encodings. For each block object specification, a respective query is generated according to the user-selected data field icons that specify the visual encodings for the respective block object specification. Each query is executed to retrieve a respective set of tuples from the data source. Each tuple comprises data values for data fields corresponding to the user-selected data field icons. A data visualization is generated in accordance with the layout type, the mark type, and the visual encodings specified in each of the block object specifications, wherein each tuple in each of the respective set of tuples is represented as a visual mark in the 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: 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: At a computer system a graphical user interface is displayed that includes a schema region displaying data field icons, each data field icon representing a respective data field, and a canvas region including block cards. Each block card specifies a respective layout type that is one of: a row-based arrangement, a column-based arrangement, or an inline arrangement. Each block card is generated in response to user placement of one or more respective data field icons onto the canvas region. For each block card, a respective database query is generated and executed to retrieve tuples of data for data fields associated with the respective block card. For each retrieved tuple, a respective two-dimensional grid position, including a respective row index and a respective column index, is computed. A data visualization is generated and displayed. Each retrieved tuple is represented as a visual mark displayed at its respective computed grid position.

Abstract: At a computer system a number of interrelated block object specifications are generated according to user placement of data field icons on a canvas region. Each block object specification includes a layout type, a mark type, and one or more visual encodings. For each block object specification, a respective query is generated according to the user-selected data field icons that specify the visual encodings for the respective block object specification. Each query is executed to retrieve a respective set of tuples from the data source. Each tuple comprises data values for data fields corresponding to the user-selected data field icons. A data visualization is generated in accordance with the layout type, the mark type, and the visual encodings specified in each of the block object specifications, wherein each tuple in each of the respective set of tuples is represented as a visual mark in the 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: A method is performed at a computing device that includes a display, one or more processors, and memory. The device displays a data visualization based on a dataset retrieved from a database. It also displays one or more phrases in a first region. The phrases define the data visualization. The device receives user input in a second region to specify a natural language command related to the displayed data visualization. In response to the user input, the device extracts independent analytic phrases from the command, determines one or more proposed actions based on the extracted phrases, and displays the proposed actions. The device receives user selection of a first proposed action. In response, the device generates an updated data visualization. It displays the updated data visualization and displays one or more updated phrases in the first region. The updated phrases define the updated data visualization.

Abstract: A computing device receives user selection of a data source and a natural language command directed to the data source. The device identifies a first keyword and a second keyword in the natural language command. The first keyword corresponds to a first data field from the data source and the second keyword expresses a limit on a range of data values for the first data field. The device generates a visual specification that 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 determine characteristics of visual marks in a data visualization according to the second keyword, and each of the visual variables is associated with a respective data field of the plurality of data fields. The device generates and displays the 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 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 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: 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 electronic 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 electronic device formulates a respective intent specification according to the request, including determining respective properties of the respective intent specification. The electronic device also generates a respective output specification. The respective output specification includes a respective recommended data visualization type.

Abstract: A computing device displays a data visualization interface and receives user selection of a data source and a natural language command directed to the data source. The device forms a first intermediate expression according to a context-free grammar and a semantic model of data fields in the data source. In accordance with a determination that the first intermediate expression omits sufficient information for generating a data visualization, the device infers the omitted information associated with the data source using one or more inferencing rules based on syntactic and semantic constraints imposed by the context-free grammar. The device forms an updated intermediate expression, and translates the updated intermediate expression into database queries. It executes the database queries to retrieve data sets from the data source, then generates and displays a data visualization of the retrieved data sets.

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 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 method modifies data visualizations based on user selected data sources and user input that specifies natural language commands requesting information from the data sources. The computer displays an initial data visualization according to a visual specification that specifies a plurality of visual variables. The computer determines one or more keywords from the natural language command and determines, based on the one or more keywords, a user intent to modify the data visualization. The computer modifies the plurality of visual variables based on the first user intent and displays a modified data visualization in accordance with the modified plurality of visual variables.

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.