Tableau Software, Inc.

Ryan Andrew Atallah

Yukiko Ishida Anonuevo

Jared Briskman

Alex Djalali

Jeffrey Ericson

Suyang Duan

Theodore Kornish

Clark Wildenradt

Qixiang Zhang

Sophia Chen

Scott A Sherman

Jeffrey Jon Weir

Jun Ho Kim

Andrew C Beers

Ruhaab Shay Markas

Yukiko Ishida Añonuevo

Eleanor Page Darby Fields

Lu Han

A method generates data analysis templates. A computing device receives, via a graphical user interface, a natural language input, which includes multiple terms. The terms include one or more data fields and/or data values from a data source. The device designates the natural language input as trigger text for a data analysis template. The trigger text includes the multiple terms. A user defines one or more rules for a subset of the terms, each rule specifying respective criteria to match a respective term in the subset. A user specifies multiple data visualizations corresponding to the trigger text. Each of the data visualizations (i) uses a respective one or more data fields, from the data source, corresponding to terms from the trigger text and (ii) has a respective data visualization type. The device generates the data analysis template according to the trigger text, the user definition, and the user specification.

Generating data analysis dashboard templates for selected data sources

A computer system obtains a data model that encodes a data source as a tree of logical tables. Each logical table includes respective data fields of the data source. For each data field in the logical tables, the system automatically assigns a semantic role to the data field based on a concept obtained from a concept graph, a data type of the data field, and data values of the data field. The system determines hierarchies between the data field and other data fields in the data source based on their respective semantic roles. The system validates the data field based on the assigned semantic role and the determined hierarchies. The system displays transformations to clean the data field based on the assigned semantic role. The system transforms data values for the data field according to a user input and updates the logical tables according to the transforming.

Data preparation using semantic roles

A computer device receives a user input to specify a natural language command. In response to receiving the user input, the computer device generates a semantic interpretation for the natural language command using a trained word similarity model, based on semantic annotations for a published data source. The computer device queries the published data source based on the semantic interpretation, thereby retrieving a dataset. The computer device generates and displays a data visualization based on the retrieved dataset.

Automatic synonyms using word embedding and word similarity models

A computing device receives, in a graphical user interface, a first natural language query. The computing device parses the first natural language query, thereby identifying one or more keywords in the query. The computing device identifies one or more data sources that are relevant to the query. The computing device also identifies one or more data fields and/or data values from the data sources in the query. The computing device compares the keywords to respective trigger text for each of a plurality of data analysis templates, thereby selecting a first data analysis template. The first data analysis template includes a plurality of predefined data visualizations. The computing device generates a dashboard that includes the data visualizations using the identified data fields and/or data values, and displays the dashboard in the graphical user interface.

Using a natural language interface to correlate user intent with predefined data analysis templates for selected data sources

A method is performed at a computing device having a display, processors, and memory. A user inputs a natural language expression, directed to a selected data source, in a first region of a graphical user interface. The device parses the natural language expression and determines that the natural language expression specifies a data table from the data source either (i) directly by identifying the data table specified in the natural language expression or (ii) indirectly by identifying a data value specified in the natural language expression. The data value is a data value of a data field within the data table. In response, the device generates and displays, in the graphical user interface, a count of data rows of the data table, and a rendering of data rows and data columns of the data table.

Using a natural language interface to explore entity relationships for selected data sources

A computing device receives user selection of a data source and a lens that specifies supplemental metadata for the data source. The device receives user input to specify a natural language command directed to the data source. In response to the user input, the device determines one or more data fields and/or data values from the data source for interpreting the natural language command in accordance with the metadata from the lens. The device translates the natural language command into one or more executable database queries referencing data fields and/or data values specified in the natural language command. The device executes the one or more database queries to retrieve data from the data source. The device also generates and displays a data visualization using the retrieved data.

Using semantic models determined based on data source and context in a natural language interface for visual data analysis

A computer system obtains a data model that encodes a data source as a tree of logical tables. Each logical table includes respective data fields of the data source. For each data field in the logical tables, the system automatically assigns a semantic role to the data field based on a concept obtained from a concept graph. The system determines hierarchies between the data field and other data fields in the first data source based on a respective semantic role of the data field and semantic roles of the other data fields. The system validates the data field based on the assigned semantic role and the determined hierarchies. The system displays transformations to clean the data field based on the assigned semantic role. The system transforms data values for the data field according to a user input and updates the logical tables according to the transformed data values.

A computing device has a display, one or more processors, and memory. The memory stores one or more programs configured for execution by the one or more processors. The computing device receives, via a first region of a graphical user interface, a first natural language input related to a data source. The first natural language input includes one or more terms. In response to receiving the first natural language input, computing device generates one or more interpretations for the first natural language input, including: determining a first term of the one or more terms that is excluded from the one or more interpretations, and visually de-emphasizing the first term in the first region.

Visually correlating individual terms in natural language input to respective structured phrases representing the natural language input

A computer system receives from a messaging system a first input commencing an analytic conversation between the computer system and an electronic device. The messaging system is communicatively connected with the electronic device and executes a messaging application on the electronic device. The computer system identifies a data source corresponding to the analytic conversation. The computing system receives from the messaging system a second input specifying a natural language command related to the data source. The computer system generates an interpretation corresponding to the natural language command and queries the data source according to the interpretation. The computer system generates a first data visualization using a first subset of data fields from the data source and sends a first representation of the first data visualization to the messaging system, for display on the electronic device.

Conversational natural language interfaces for data analysis

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).

Using dynamic entity search during entry of natural language commands for visual data analysis

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.

Interactive data visualization interface for data and graph models

A computing system receives from a messaging system a first input identifying a data source. The computing system receives from the messaging system a second input specifying a natural language command related to the data source. The system translates the second input into a set of parameters for building a first data visualization from the data source. The system calls one or more API functions provided by a data visualization server, and the computing system receives from the data visualization server a first data visualization that includes a first subset of data fields from the data source. The system generates a representation of the first data visualization and sends the representation to the messaging system, which in turn sends the representation to an electronic device for display on the device.

A method generates a data visualization dashboard using a natural language interface. The method is performed at a computing device having a display, one or more processors, and memory. A user specifies a natural language input directed to a data source, using a first region of a graphical user interface. In response, the computing device parses the natural language input to identify one or more database entities from the data source. For each of the database entities, the computing device determines one or more respective analytical operations for the respective database entity. The device then generates a dashboard, which includes a plurality of data visualizations corresponding to distinct combinations of the analytical operations. The first dashboard is displayed in a second region of the graphical user interface, distinct from the first region.

Using a natural language interface to generate dashboards corresponding to selected data sources

A method processes natural language commands (e.g., for data visualization). The method includes obtaining word embeddings (e.g., word vectors) for a set of words (or phrases) of a natural language. In some implementations, the set of words includes high-frequency or common words of the natural language. The method also includes training a word similarity model to identify similar words based on the word embeddings and a synonym database (a large lexical database, such as a thesaurus). The method also includes generating semantic annotations for a published data source using the trained word similarity model, based on the synonym database, and the word embeddings. The method also includes generating a semantic interpretation for a natural language command based on the semantic annotations for the published data source. The method also includes querying the published data source based on the sematic interpretation, thereby retrieving a dataset.

A computing device displays a first data visualization that includes a first plurality of data marks. Each of the data marks corresponds to a respective distinct data value of a first data field from a dataset. In response to user selection of a first data mark that corresponds to a first data value of the first data field, the device displays a first data widget that includes one or more user-selectable affordances. In response to user selection of a first affordance of the affordances, the device displays a first drill down widget. The device receives user selection of a second data field from the dataset in the drill down widget. In response to the user selection, the device generates a second data visualization that includes a second plurality of data marks and displays the second data visualization.

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Palo Alto, CA, United States of America

Ryan Andrew Atallah