Tableau Software, Inc.

Justin Talbot

Bora Beran

Jock Douglas Mackinlay

Jun Ho Kim

Christopher Richard Stolte

Robin Stewart

Daniel Cory

Marc Rueter

Christian Gabriel Eubank

Ross Thomas Bunker

Nicolas Ratigan Borden

Thomas Nhan

Jeffrey Mark Booth, Jr

Teresa Lynn Roberts

Amy Nicole Forstrom

Jiyoung Oh

Jeannine Frazier Boone

Roger Hau

Ian A Coe

Sean Boon

David Pace

Susan Denise Doan

Elaine Weatherfield Sulc

Clark Wildenradt

Allan Folting

Lauren Christina Lum

Jeffrey Jon Weir

Andrew C Beers

Ginger J Gloystein

Daniel Ting

Russell Steven Paul-Jones

Jeffrey Mark Booth Jr,

A method displays a first data visualization according to user association of data fields with displayed data visualization properties. The data visualization includes visual data marks corresponding to data values for data fields in a dataset. A user selects a subset of the visual data marks. A popup summary is displayed that includes data value distributions for several data fields from the dataset. In the popup summary, a second user input is detected corresponding to a first data field whose data value distribution is displayed in the popup summary. The second user input associates the first data field with a first visualization property of the displayed data visualization properties. In response, a second data visualization is displayed according to association of data fields, including the first data field, with displayed data visualization properties.

Data visualization user interface with summary popup that includes interactive objects

A computer system displays a data visualization in a data visualization user interface. In response to detecting a user input that selects a subset of visual data marks from the data visualization, the computer system displays a summary of the selected subset of visual data marks in a summary tab of a view data window. When the computer system determines that the selected subset of visual marks (1) corresponds to a plurality of data fields and (2) references a row-level calculation that uses logical fields from at least two logical tables of a plurality of logical tables of a data model, the computer system (i) generates a representation of the row-level calculation and (ii) displays the representation of the row-level calculation as a first tab in the view data window, distinct from the summary tab.

System and method for visually analyzing row-level calculations for data visualizations across multiple data tables including displaying separate tabs for the row-level calculations and visual data marks summary

A computer receives user selection of data fields from a data source and user placement of the data fields into a shelf region of a user interface. The computer obtains a data model encoding the data source as a tree of related logical tables. Each logical table includes logical fields, each of which corresponds to either a dimension or a measure. The computer generates a dimension subquery for the dimensions. The computer also generates, for each measure, an aggregated measure subquery grouped by the dimensions. The computer forms a final query by joining the dimension subquery to each of the aggregated measure subqueries. The computer subsequently executes the final query and displays a data visualization according to the results of the final query.

Utilizing appropriate measure aggregation for generating data visualizations of multi-fact datasets

A method for generating schemas to compress storage of denormalized data includes receiving user selection of an initial data table and constructing a snowflake schema to decompose the initial data table into a plurality of tables. Tables in the plurality of tables that are related to one another are connected by foreign keys. The snowflake schema is constructed according to a cost model that balances the cost of storing the plurality of tables and the cost of storing the foreign keys. The method also includes transforming data in the initial data table into the plurality of tables according to the snowflake schema.

Database normalization using statistical analysis

A data visualization application receives user actions to (i) associate a first data field with a first shelf and (ii) associate second and third data fields with a second shelf. The method receives an additional user action to specify a nested sort according to data for the first and third data fields. The method generates and displays a data visualization in accordance with the received actions, including: (i) partitioning retrieved data into groups of tuples, each tuple including data for the data fields, and each group of tuples corresponding to a distinct value of the second data field, (ii) forming panes, each pane corresponding to a respective group of tuples, and (iii) within each pane, displaying data marks, each data mark corresponding to a respective tuple.

Nested sorting of data marks in data visualizations

The method receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with data fields and each data field is either a dimension or a measure. From an object model of the data source, the method identifies a minimal subtree that includes all of the dimension data fields and constructs a query from the minimal subtree. The method executes the query against the data source to retrieve a set of tuples, each tuple comprising a unique ordered combination of data values for the dimension data fields. For each tuple, the method forms an extended tuple by appending aggregated data values corresponding to each measure data field. The method then builds and displays a data visualization according to the data fields in the extended tuples and according to the visual variables to which the data fields are associated.

Generating data visualizations according to an object model of selected data sources

A computer receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with either data fields (e.g., dimension and/or measures) or filters. The computer obtains a data model encoding the data source as a tree of related logical tables. Each logical table includes logical fields, each of which corresponds to either a data field or a calculation that spans logical tables. The computer generates a dimension subquery for the dimensions and the filters. The computer also generates, for each measure, an aggregated measure subquery grouped by the dimensions. The computer forms a final query by joining the dimension subquery to each of the aggregated measure subqueries. The computer subsequently executes the final query and displays a data visualization according to the results of the final query.

An electronic device displays a chart in accordance with contents of a plurality of shelf regions of a data visualization interface. The chart includes visual marks representing a set of data. The electronic device generates a visual analytic object by applying a first analytic operation to the set of data represented by the visual marks, and displays the visual analytic object superimposed over the chart. The device detects user input to select the displayed visual analytic object and place an icon representing the visual analytic object onto a first shelf region of the plurality of shelf regions. In response to the user input, the electronic device updates content of the first shelf region to include an association with the first analytic operation corresponding to the visual analytic object.

Systems and methods for using analytic objects in a dynamic data visualization interface

A user selects a data source, and a computer displays a data visualization in a data visualization user interface according to the data source. The data visualization includes visual data marks representing data from the data source. The user selects a subset of the visual data marks. In response, the computer displays a view data window having a summary of the selected data marks. The computer obtains a data model encoding the data source as a tree of logical tables. The computer identifies aggregate measures corresponding to the selected data marks, where each aggregate measure is aggregated from logical tables of the data model. The computer displays each aggregate measure in the view data window. The computer also displays, in the view data window, one or more level of detail calculations referenced in the selected subset of visual data marks.

Methods and user interfaces for visually analyzing data visualizations with row-level calculations

An electronic device displays a chart, which includes a first set of visual marks that represent values derived from a set of data. The device concurrently displays a chart-calculations-options area. The chart-calculations-options area displays a first plurality of options for type of calculation. The device detects user selection of (i) a first option, from the first plurality of options, for type of calculation and (ii) a second option corresponding to a parameter for the selected first option. In response to detecting the user selection, the device visually distinguishes visual marks in a second set of visual marks in the chart. The second set of visual marks is distinct from the first set of visual marks and corresponds to the first option for type of calculation and the second option corresponding to the parameter for the selected first option.

User interface for generating data visualizations that use table calculations

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.

Methods and user interfaces for generating level of detail calculations for data visualizations

The process receives a visual specification, which specifies data sources, visual variables, and data fields from the data sources. Each visual variable is associated with one or more data fields and each data field is either a dimension or a measure. For each measure m, the process identifies a set R(m) consisting of dimensions that are reachable from the measure by a sequence of many-to-one relationships in an object model for the data sources. For each distinct set R, the process forms a data field set S, consisting of each dimension in R and each measure m for which R(m)=R. For each set S and for each measure in the set S, the process aggregates values of the measure according to the dimensions in S. The process builds a data visualization according to the data fields in the set S and the visual variables they are associated with.

Using an object model of heterogeneous data to facilitate building data visualizations

A method displays a chart including visual marks representing a dataset. The chart is based on content of displayed shelf regions, each determining a respective characteristic of the visual marks. The method generates a visual analytic object by applying a first analytic operation to the dataset, and displays the visual analytic object over the chart. The method detects user input to select the displayed visual analytic object and place an icon representing the visual analytic object onto a first shelf region. The first shelf region defines a first characteristic of the visual marks. In response to the user input, the method updates content of the first shelf region to specify association with the first analytic operation and updates the first characteristic of the visual marks in the chart based on values of the set of data represented by the visual marks relative to values computed by the first analytic operation.

The method receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with data fields and each data field is a dimension or a measure. The method forms dimension tuples comprising distinct ordered combinations of data values for the dimensions D. For each measure, the method: forms a set S of the dimensions D plus dimensions from a primary key corresponding to the measure; retrieves intermediate tuples containing the fields in S and the measure, without aggregation; and aggregates the intermediate tuples according to the dimensions D. For each dimension tuple, the method forms an extended tuple by appending the aggregated data values corresponding to each measure field. The method then builds and displays a data visualization according to the extended tuples and the visual variables to which the data fields are associated.

An electronic device displays a chart, which includes visual marks that represent calculated values derived from a set of data, and panes formed by intersection of rows and columns. A current focus is on a first visual mark at a first position. The device concurrently displays a chart-calculations-options area, which includes display options for the type of calculation and the type of data partitioning. Visual marks in a first set of visual marks are visually distinguished from other visual marks. The first set of visual marks includes the first visual mark, and corresponds to a first selected option for the type of calculation and a first selected option for the type of data partitioning. The device detects an input that selects a second option for the type of data partitioning. In response, the device visually distinguishes visual marks in a second set of visual marks in accordance with the selection.

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Seattle, WA, United States of America

Justin Talbot