Tableau Software, Inc.

Christian Gabriel Eubank

Justin Talbot

Nicolas Ratigan Borden

Daniel Cory

Jeffrey Mark Booth, Jr

Amy Nicole Forstrom

Thomas Nhan

David Pace

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 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 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.

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 database engine receives a query batch of database queries from a client. The database engine identifies one or more object model queries from the query batch. Each object model query includes an outer-most outer-join that joins a respective dimension subquery and respective aggregated measure subqueries. The database engine forms a plurality of candidate subqueries by peeling off the respective outer-most outer-join for each of the object model queries. The database engine then fuses at least some of the plurality of candidate subqueries to form a set of optimized subqueries. The set of optimized subqueries has fewer subqueries than the plurality of candidate queries. The database engine also forms an optimized execution plan based on the set of one or more optimized subqueries. The database engine subsequently obtains a result set from the database based on the optimized execution plan, and returns the result set to the client.

Optimizing complex database queries using query fusion

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 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 that corresponds to either a data field or a calculation that spans logical tables. The computer constructs a query corresponding to the visual specification. The query references two logical tables connected via multiple linking fields. To generate the query, the computer generates a first left-outer join of the two tables, forms a hidden join key expression using a coalesce, according to the data model, and generates another left-outer join using the hidden join key expression. The computer executes the query and displays a data visualization according to the results of the query.

Join key recovery and functional dependency analysis to generate database queries

Embodiments are directed to visualizing data. A computed field that includes input fields and an object model that includes objects may be provided. One or more of the objects may be determined based on the input fields such that each input field corresponds to a determined object. A sub-graph may be generated based on the determined objects. Zero or more determined objects may be pruned from the sub-graph based on a traversal of the sub-graph and characteristics of the determined objects such that each pruned object may be determined to be an attribute of at least one of the determined objects. In response to the iterative pruning providing one remaining determined object in the sub-graph, assigning the remaining determined object as a parent of the computed field.

Parenting computed fields with data objects

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.

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Seattle, WA, United States of America

Christian Gabriel Eubank