IP Library › Granted Patent US 12,411,861
Granted Patent B2
US 12,411,861 · App. 18/639,946 · Granted Sep 9, 2025

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

Inventors: Christian Eubank (Seattle, WA); Justin Talbot (Seattle, WA)
Assignee: Tableau Software, Inc.
G06F16/248G06F16/2282G06F16/24556G06F16/2456
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Quick Facts
Patent No.
US 12,411,861
App. No.
18/639,946
Granted
Sep 9, 2025
Kind
B2
Abstract

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.

Claims (72)

1. A method of generating data visualizations, comprising:

at a computer having a display, one or more processors and memory storing one or more programs configured for execution by the one or more processors:

receiving user selection of one or more data fields from a data source and user placement of the one or more data fields into a shelf region of a user interface, the data source including a plurality of data fields and each of the data fields is either a dimension or a measure;

in accordance with receiving the user selection and the user placement:

obtaining a data model encoding the data source as a tree of logical tables, each logical table including a respective one or more logical fields, each logical field corresponding to a measure data field or a dimension data field of the data source, wherein each edge of the tree connects two logical tables that are related;

generating a dimension subquery based on logical tables that supply the data fields for the dimensions;

generating, for each measure, based on the logical tables that supply the data fields for the respective measure, a respective aggregated measure subquery grouped by the dimensions;

forming a final query by joining, using the dimensions, the dimension subquery to each of the aggregated measure subqueries;

executing the final query against the data source to retrieve tuples that comprise distinct ordered combinations of data values for the data fields; and

generating and displaying, in the user interface, a data visualization according to the data fields in the tuples.

2. The method of claim 1 , further comprising:

in accordance with receiving the user selection of the one or more data fields and the user placement of the one or more data fields into the shelf region:

generating a visual specification that specifies the data source and a plurality of visual variables, wherein each of the visual variables is associated with either a respective data field of the data source or a respective filter.

3. The method of claim 1 , wherein generating, for each measure, the respective aggregated measure subquery comprises:

computing a measure sub-tree of the tree of logical tables, wherein the measure sub-tree is a minimum sub-tree required to supply the data fields for the respective measure;

computing a dimension-filter sub-tree of the tree of logical tables, wherein the dimension-filter sub-tree is a minimum sub-tree required to supply all physical inputs for the dimensions;

compiling the measure sub-tree to obtain a measure join tree and compiling the dimension-filter sub-tree to obtain a dimension-filter join tree; and

obtaining the respective aggregated measure subquery based on the measure join tree and the dimension-filter join tree.

4. The method of claim 3 , wherein compiling the measure sub-tree comprises inner joining logical tables in the measure sub-tree to obtain the measure join tree.

5. The method of claim 3 , wherein computing the dimension-filter sub-tree comprises inner joining logical tables in the dimension-filter sub-tree that are shared with the measure sub-tree, and left-joining logical tables in the dimension-filter sub-tree that are not shared with the measure sub-tree, to obtain the dimension-filter join tree.

6. The method of claim 3 , further comprising:

after obtaining the measure join tree and the dimension-filter join tree:

layering calculations and filters over the measure join tree and the dimension-filter join tree to obtain an updated measure sub-tree and an updated dimension-filter sub-tree, respectively; and

de-duplicating the updated dimension-filter sub-tree by applying a group-by operation that uses the dimensions and linking fields that include (i) keys from relationships between the logical tables and (ii) data fields of calculations shared with the measure sub-tree, to obtain a de-duplicated dimension-filter sub-tree.

7. The method of claim 6 , wherein obtaining the respective aggregated measure subquery based on the measure join tree and the dimension-filter join tree includes:

combining the de-duplicated dimension-filter sub-tree with the updated measure sub-tree.

8. The method of claim 7 , wherein combining the de-duplicated dimension-filter sub-tree with the updated measure sub-tree comprises:

determining if the de-duplicated dimension-filter sub-tree contains a filter;

in accordance with a determination that the de-duplicated dimension-filter sub-tree contains a filter, inner-joining the updated measure sub-tree with the de-duplicated dimension-filter sub-tree; and

in accordance with a determination that the de-duplicated dimension-filter sub-tree does not contain a filter, left outer-joining the updated measure sub-tree with the de-duplicated dimension-filter sub-tree.

9. The method of claim 7 , further comprising:

in accordance with a determination that the keys indicate a many-to-one relationship between a first logical table and a second logical table of the tree of logical tables:

including the first logical table and the second logical table in the measure sub-tree, thereby avoiding the group-by in a de-duplication operation for the first logical table and the second logical table.

10. The method of claim 7 , further comprising:

in accordance with a determination that the dimension-filter sub-tree joins against the measure sub-tree exclusively along many-to-one and one-to-one links, replacing tables shared by the measure sub-tree and the dimension-filter sub-tree with the de-duplicated dimension-filter sub-tree.

11. The method of claim 1 , wherein generating the dimension subquery comprises inner-joining a first one or more logical tables in the tree of logical tables, wherein each logical table of the first one or more logical tables supplies the data fields for a dimension.

12. The method of claim 1 , wherein forming the final query comprises:

joining the dimensions subquery and the aggregated measure subqueries on the dimensions using outer joins; and

applying a COALESCE to each outer join.

13. The method of claim 1 , wherein forming the final query comprises, in accordance with a determination that the data visualization has no dimensions, performing a full outer join between the aggregated measure subqueries.

14. The method of claim 1 , wherein the respective one or more logical fields include a first logical field that corresponds to a calculation that spans one or more logical tables.

15. A computer system for generating data visualizations, comprising:

one or more processors; and

memory, wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs include instructions for:

receiving user selection of one or more data fields from a data source and user placement of the one or more data fields into a shelf region of a user interface, the data source including a plurality of data fields and each of the data fields is either a dimension or a measure;

in accordance with receiving the user selection and the user placement:

obtaining a data model encoding the data source as a tree of logical tables, each logical table including a respective one or more logical fields, each logical field corresponding to a measure data field or a dimension data field of the data source, wherein each edge of the tree connects two logical tables that are related;

generating a dimension subquery based on logical tables that supply the data fields for the dimensions;

generating, for each measure, based on the logical tables that supply the data fields for the respective measure, a respective aggregated measure subquery grouped by the dimensions;

forming a final query by joining, using the dimensions, the dimension subquery to each of the aggregated measure subqueries;

executing the final query against the data source to retrieve tuples that comprise distinct ordered combinations of data values for the data fields; and

generating and displaying, in the user interface, a data visualization according to the data fields in the tuples.

16. The computer system of claim 15 , wherein the instructions for generating, for each measure, the respective aggregated measure subquery include instructions for:

computing a measure sub-tree of the tree of logical tables, wherein the measure sub-tree is a minimum sub-tree required to supply the data fields for the respective measure;

computing a dimension-filter sub-tree of the tree of logical tables, wherein the dimension-filter sub-tree is a minimum sub-tree required to supply all physical inputs for the dimensions;

compiling the measure sub-tree to obtain a measure join tree and compiling the dimension-filter sub-tree to obtain a dimension-filter join tree; and

obtaining the respective aggregated measure subquery based on the measure join tree and the dimension-filter join tree.

17. The computer system of claim 16 , wherein the instructions for compiling the measure sub-tree include instructions for inner joining logical tables in the measure sub-tree to obtain the measure join tree.

18. The computer system of claim 16 , wherein the instructions for computing the dimension-filter sub-tree include instructions for inner joining logical tables in the dimension-filter sub-tree that are shared with the measure sub-tree, and left-joining logical tables in the dimension-filter sub-tree that are not shared with the measure sub-tree, to obtain the dimension-filter join tree.

19. The computer system of claim 16 , the one or more programs further include instructions for:

after obtaining the measure join tree and the dimension-filter join tree:

layering calculations and filters over the measure join tree and the dimension-filter join tree to obtain an updated measure sub-tree and an updated dimension-filter sub-tree, respectively; and

de-duplicating the updated dimension-filter sub-tree by applying a group-by operation that uses the dimensions and linking fields that include (i) keys from relationships between the logical tables and (ii) data fields of calculations shared with the measure sub-tree, to obtain a de-duplicated dimension-filter sub-tree.

20. A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having a display, one or more processors, and memory, the one or more programs comprising instructions for:

receiving user selection of one or more data fields from a data source and user placement of the one or more data fields into a shelf region of a user interface, the data source including a plurality of data fields and each of the data fields is either a dimension or a measure;

in accordance with receiving the user selection and the user placement:

obtaining a data model encoding the data source as a tree of logical tables, each logical table including a respective one or more logical fields, each logical field corresponding to a measure data field or a dimension data field of the data source, wherein each edge of the tree connects two logical tables that are related;

generating a dimension subquery based on logical tables that supply the data fields for the dimensions;

generating, for each measure, based on the logical tables that supply the data fields for the respective measure, a respective aggregated measure subquery grouped by the dimensions;

forming a final query by joining, using the dimensions, the dimension subquery to each of the aggregated measure subqueries;

executing the final query against the data source to retrieve tuples that comprise distinct ordered combinations of data values for the data fields; and

generating and displaying, in the user interface, a data visualization according to the data fields in the tuples.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 14, 2025
From: EUBANK, CHRISTIAN GABRIEL; TALBOT, JUSTIN
To: TABLEAU SOFTWARE, INC.
Reel/Frame 072027/0437 →
CHANGE OF NAME Recorded Aug 14, 2025
From: TABLEAU SOFTWARE, INC.
To: TABLEAU SOFTWARE, LLC
Reel/Frame 072442/0963 →
Continuity (4)
Continuation 16570969 · Sep 13, 2019
Continuation In Part 16236611 · Dec 30, 2018
Provisional Application 62748968 · Oct 22, 2018
Related Publication 20240265025A1 · Aug 8, 2024
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