VISUALIZATION SUGGESTION APPLICATION PROGRAMMING INTERFACE

• US 20160085835A1
• Filed: 09/18/2014
• Published: 03/24/2016
• Est. Priority Date: 09/18/2014
• Status: Active Grant

• Current Assignee: Business Objects Incorporated (SAP SE)
• Original Assignee: Business Objects Incorporated (SAP SE)
• Inventors: Kumar, Viren, Wong, Johnson, Moser, Flavia
• Granted Patent: US 9,830,370 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/287 Visualization; Browsing

Abstract

A dataset and some user selected columns of the dataset are received by a statistical analysis module for analysis. The statistical analysis module generates a score for each unselected column of the dataset based on statistical analysis of the unselected columns and all or a subset of the selected columns. A ranking of the unselected columns is presented to the user for selection of one additional column of the dataset, after which the remaining unselected columns are re-ranked according to their associated scores and once again displayed to the user. The user may continue selecting from among the ranked columns until a threshold number of columns has been selected, at which point the user may deselect a selected column in order to continue selecting additional columns. A visualization suggestion application program interface then matches the selected columns with compatible visualization configurations and presents some of these visualizations to the user.

Claims

1. A method comprising:

accessing a dataset and a user selection of at least one column of the dataset by a statistical analysis module including at least one hardware processor;

analyzing the dataset, by the statistical analysis module, to generate a score for each unselected column of the dataset based on a degree of dependency between each of the unselected columns and the at least one selected column;

iteratively displaying a ranking of the unselected columns according to the scores, and accessing a user selection of one more column by the statistical analysis module until a threshold number of columns has been selected;

accessing the selected columns of the dataset by a visualization suggestion module including at least one hardware processor; and

selecting, by the visualization suggestion module, a specified number of visualization configurations compatible with the selected columns from a set of visualization configurations and providing the compatible visualization configurations to a user.

2. The method of claim 1, further comprising:

accessing user input by the statistical analysis module, the input including a deselection of one of the selected columns based on the threshold number of columns being selected.

3. The method of claim 2, wherein the dataset accessed by the statistical analysis module includes at least one online analytical processing (OLAP) cube with each column of the cube classified as a measure or classified as a dimension, the method further comprising:

determining, by the statistical analysis module, which type of analysis to perform on each of the unselected columns of the dataset based on;

the classification of the at least one column selected by a user; and

the unselected column being classified as a dimension and a cardinality of the dimension satisfying specified criteria.

4. The method of claim 3, further comprising:

aggregating over unselected dimensions of the cube based on the unselected dimensions having cardinality less than 10;

analyzing the dataset by performing an analysis of variance (ANOVA) test on the unselected columns of the dataset and on aggregated data; and

generating a score for each unselected column of the dataset based on an effect size of the ANOVA test.

5. The method of claim 3, further comprising:

aggregating over unselected dimensions of the cube based on the unselected dimensions having cardinality of at least 20;

analyzing the dataset by performing a correlation coefficient test on the unselected columns of the dataset and on aggregated data; and

generating a score for each unselected column of the dataset based on a p-value of the correlation coefficient test.

6. The method of claim 3, further comprising:

determining, by the statistical analysis module, that multiple types of analysis be performed on an unselected column of the dataset;

generating a score for said unselected column based on an average of multiple scores generated for said unselected column by the multiple types of analysis; and

generating a null score for an unselected column based on the unselected column being classified as a dimension and the cardinality of the dimension failing to satisfy the specified criteria.

7. The method of claim 3, wherein a visualization configuration specifies how a set of columns should be arranged and represented in a chart and includes constraints regarding the columns, the method further comprising:

determining, by the visualization suggestion module, that a visualization configuration is compatible with the selected columns based on;

a number of selected columns being equal to a number of columns in the visualization;

a number of selected columns classified as dimensions being equal to a number of columns classified as dimensions in the visualization;

a number of selected columns classified as measures being equal to a number of columns classified as measures in the visualization; and

the selected columns satisfying constraints of the visualization regarding columns.

8. A system comprising:

a statistical analysis module including at least one processor and configured to;

access a dataset and a user selection of at least one column of the dataset;

analyze the dataset to generate a score for each unselected column of the dataset based on a degree of dependency between each of the unselected columns and the at least one selected column; and

iteratively display a ranking of the unselected columns according to the scores, and accessing a user selection of one more column by the statistical analysis module until a threshold number of columns has been selected; and

a visualization suggestion module including at least one processor and configured to;

access the selected columns of the dataset;

select a specified number of visualization configurations compatible with the selected columns from a set of visualizations; and

provide the compatible visualization configurations to a user.

9. The system of claim 8, wherein the statistical analysis module is configured to access user input including a deselection of one of the selected columns based on the threshold number of columns being selected.

10. The system of claim 9, wherein the dataset accessed by the statistical analysis module includes at least one online analytical processing (OLAP) cube with each column of the cube classified as a measure or classified as a dimension, wherein the statistical analysis module is configured to:

determine which type of analysis to perform on each of the unselected columns of the dataset based on;

the classification of the at least one column selected by a user; and

the at least one column selected by a user being classified as a dimension and cardinality of the dimension satisfying specified criteria.

11. The system of claim 10, wherein the statistical analysis module is configured to:

aggregate over unselected dimensions of the cube based on the unselected dimension having cardinality less than 10;

analyze the dataset by performing an analysis of variance (ANOVA) test on the unselected columns of the dataset and on aggregated data; and

generate a score for each unselected column of the dataset based on an effect size of the ANOVA test.

12. The system of claim 10, wherein the statistical analysis module is configured to:

aggregate over unselected dimensions of the cube based on the unselected dimension having cardinality of at least 20;

analyze the dataset by performing a correlation coefficient test on the unselected columns of the dataset and on aggregated data; and

generate a score for each unselected column of the dataset based on a p-value of the correlation coefficient test.

13. The system of claim 10, wherein the statistical analysis module is configured to:

determine that multiple types of analysis be performed on an unselected column of the dataset;

generate a score for said unselected column based on an average of multiple scores generated for said unselected column by the multiple types of analysis; and

generate a null score for an unselected column based on the at least one column selected by the user being classified as a dimension and the cardinality of the dimension failing to satisfy the specified criteria.

14. The system of claim 10, wherein:

a visualization configuration specifies how a set of columns should be arranged and represented in a chart and includes constraints regarding the columns; and

the visualization suggestion module is configured to determine that a visualization configuration is compatible with the selected columns based on;

a number of selected columns being equal to a number of columns in the visualization;

a number of selected columns classified as dimensions being equal to a number of columns classified as dimensions in the visualization;

a number of selected columns classified as measures being equal to a number of columns classified as measures in the visualization; and

the selected columns satisfy constraints of the visualization regarding columns.

15. A non-transitory machine-readable storage medium including instructions that, when executed on at least one processor of a machine, cause the machine to perform the operations comprising:

accessing a dataset and a user selection of at least one column of the dataset by a statistical analysis module including at least one hardware processor;

analyzing the dataset, by the statistical analysis module, to generate a score for each unselected column of the dataset based on a degree of dependency between each of the unselected columns and the at least one selected column;

iteratively displaying a ranking of the unselected columns according to the scores, and accessing a user selection of one more column by the statistical analysis module until a threshold number of columns has been selected;

accessing the selected columns of the dataset by a visualization suggestion module including at least one hardware processor; and

selecting, by the visualization suggestion module, a specified number of visualization configurations compatible with the selected columns from a set of visualizations and providing the compatible visualization configurations to a user.

16. The non-transitory machine-readable storage medium of claim 15, wherein the operations further comprise:

accessing user input by the statistical analysis module, the input including a deselection of one of the selected columns based on the threshold number of columns being selected.

17. The non-transitory machine-readable storage medium of claim 16 wherein:

the dataset accessed by the statistical analysis module includes at least one online analytical processing (OLAP) cube with each column of the cube classified as a measure or classified as a dimension; and

the operations further comprise determining, by the statistical analysis module, which type of analysis to perform on each of the unselected columns of the dataset based on;

the classification of the at least one column selected by the user; and

the at least one column selected by the user being classified as a dimension and a cardinality of the dimension satisfying specified criteria.

18. The non-transitory machine-readable storage medium of claim 17, wherein the operations further comprise:

aggregating over unselected dimensions of the cube based on an unselected dimension having cardinality less than 10;

analyzing the dataset by performing an analysis of variance (ANOVA) test on the unselected columns of the dataset and on aggregated data; and

generating a score for each unselected column of the dataset based on an effect size of the ANOVA test.

19. The non-transitory machine-readable storage medium of claim 17, wherein the operations further comprise:

determining, by the statistical analysis module, that multiple types of analysis be performed on an unselected column of the dataset;

generating a score for said unselected column based on an average of multiple scores generated for said unselected column by the multiple types of analysis; and

generating a null score for an unselected column based on the at least one column selected by the user being classified as a dimension and the cardinality of the dimension failing to satisfy the specified criteria.

20. The non-transitory machine-readable storage medium of claim 17, wherein:

a visualization configuration specifies how a set of columns should be arranged and represented in a chart and includes constraints regarding the columns; and

the operations further comprise determining, by the visualization suggestion module, that a visualization configuration is compatible with the selected columns based on;

a number of selected columns being equal to a number of columns in the visualization;

a number of selected columns classified as dimensions being equal to a number of columns classified as dimensions in the visualization;

a number of selected columns classified as measures being equal to a number of columns classified as measures in the visualization; and

the selected columns satisfy constraints of the visualization regarding columns.