Tableau Software, Inc.

Microsoft Technology Licensing, LLC

Daniel Cory

Justin Talbot

Thomas Nhan

Jeffrey Mark Booth, Jr

Matthew John Androski

Christian Gabriel Eubank

Teresa Lynn Roberts

Amy Nicole Forstrom

Jiyoung Oh

Michael John Arvold

Jeannine Frazier Boone

Ian A Coe

Roger Hau

Britta Claire Nielsen

Charles David Ellis

David Francis Gainer

Andrew James Becker

Ross Thomas Bunker

David Pace

Susan Denise Doan

Elaine Weatherfield Sulc

Alexander A Babanov

Miltiadis Vafiadis

Nicolas Ratigan Borden

Roy Simkhay

Lauren Christina Lum

Allan Folting

Vivek Anup Maharajh

Daniel William DeKlotz

Simon Loftus Peyton Jones

Monica McAmis

Jeremy R Pankratz

Johnny Hsienchow Lee

Kristin Michele Tolle

Sander M Viegers

Benjamin Edward Rampson

Scott A Sherman

Christopher Alan Hays

David Benjamin Lee

Maneesh Agrawala

Brian Scott Ruble

Barry Christopher Allyn

Jason David Carlson

Marcus Eduardo Markiewicz

James Baker

Fang Wang

B Scott Ruble

Adam Clyde Eversole

John A Bocharov

Kelly J Lynch

Anushka Anand

Austin David Dahl

Sissie Ling-le Hsiao

Abhishek Joshi

Benjamin L Chronister

Randall Moss

Randall L Davis

Miranda Rose Rensch

Justin Frederick Rockwood

Thomas D Taylor

Andrew John Verprauskus

Koichi Tsunoda

Douglas Alan Thomae

Stephen Howard Pellegrin

Russell Steven Paul-Jones

Miltos Vafiadis

Jason Scott King

Lu Sien Tan

Hailei Chen

John Diaa Fahmy Morcos

Jeffrey Mark Booth Jr,

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 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 user interface displays: a first column comprising non-editable input strings retrieved from a data field; a second column comprising editable output strings initialized from the data field; and an expression window displaying a transformation function &#x192;. The computer iteratively processes user inputs, each user input i providing a sample row transformation to edit an ith output string t. Some user inputs i designate a contiguous substring ssof the corresponding input string s. The contiguous substring expresses a causal basis for transforming the input string s. into the output string t. The computer updates the transformation function &#x192; according to the provided sample row transformations so that: &#x192;(s)=t, . . . , &#x192;(s)=t; the transformation function &#x192; specifies text or string position of at least one contiguous substring; and &#x192; has minimal branching among possible transformation functions that satisfy the samples. The computer displays the updated transformation function &#x192; in the expression window.

Generating visually simplified calculation expressions corresponding to user manipulation of textual data elements

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

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

Embodiments are directed to managing event information. A plurality of events associated with entities may be provided. A plurality of state types may be determined based on the plurality of events such that each state type is associated with a state. State keys associated with each entity may be determined based on events associated with each entity and the state types. A state stream for each entity may be provided based on their state keys such that each state stream may be an ordered sequence of the keys associated with each entity. In response to a query that includes a pattern filter, the pattern filter may be employed to determine a portion of the entities based on the state stream for each entity such that the pattern filter matches the state stream for each of the portion of the entities.

Event sequence analysis

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.

A method generates a data source. A user selects a first data set from a displayed object model of a database. The object model includes data sets linked visually by many-to-one relationships to form a tree. The user selects a data field in the first data set and specifies a filter condition. The system identifies a second data set in the tree, and joins the first and second data sets. When the second data set is related to the first data set via one or more many-to-one relationships, rows of the second data set are joined to rows of the first data set that satisfy the first filter condition. When the second data does not satisfy the condition, the second data set is joined to rows of the first data set that satisfy the first filter condition, and all rows of the second data set are included in the generation.

One way cascading of attribute filters in hierarchical object models

A computing device displays a data visualization interface and receives user selection of a data source. The computing device also receives user input to select a measure data field and a dimension data field. In response to the user input, the computing device: generates a custom calculation that aggregates data for the measure data field, grouped by distinct data values of the dimension data field; and stores the custom calculation as a new selectable data field, associated with a data object corresponding to the dimension data field. The computing device also receives user selection of the new selectable data field and placement of the new selectable data field onto a first shelf in a shelf region. The first shelf defines a first data visualization characteristic determined according to data values of the custom calculation. The computing device generates and displays a data visualization based on the first data visualization characteristic.

Embodiments are directed to managing data models. A data source that includes records may be provided. Source fields may be determined based on the records and the source fields may be displayed in a source panel. A data model that includes a source data object may be displayed. Relationships between the source fields may be determined based on values in the records. In response to providing a relationship between the source fields, a data object that includes a key field and one or more data fields that correspond to the relationship may be generated. The data model may be modified to include the data object and to remove the source fields that correspond to the data fields.

Interactive data modeling

An exemplary method receives user actions to (i) associate a first field with a first shelf and (ii) associate second and third fields with a second shelf. The second field is positioned at an outer-most position on the second shelf and the third field is positioned at an inner-most position on the second shelf. The method receives an additional user action to specify a nested sort according to data for the first and third 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 fields, and each group of tuples corresponding to a distinct value of the second 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.

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 displaying 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, each including one or more logical fields. Each logical field corresponds to either a data field or a calculation that spans one or more logical tables. Each edge of the tree connects two logical tables that are related. The computer identifies aggregate measures corresponding to the selected data marks, and displays each aggregate measure in the view data window.

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

An exemplary method receives user actions to (i) associate a first field with a first shelf and (ii) associate second and third fields with a second shelf. The second field is positioned at an outer-most position on the second shelf and the third field is positioned at an inner-most position on the second shelf. The method receives an additional user action to specify a nested sort according to data for the first and third 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 fields, and each group of tuples corresponds to a distinct value of the second 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.

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Daniel Cory