Ranking graphical visualizations of a data set according to data attributes

• US 8,812,947 B1
• Filed: 12/08/2011
• Issued: 08/19/2014
• Est. Priority Date: 12/08/2011
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Maoz, Hillel, Libicki, Daniel, Fink, Michael, Ho, Ronald, Lee, Dennis Julian, Matias, Yossi, Weinstein, Amit, David, Yoah Bar, Raz, Itai
• Primary Examiner(s): Hong, Stephen
• Assistant Examiner(s): Nazar, Ahamed I
• Application Number:
  US13/314,780
• Time in Patent Office: 985 Days
• Field of Search: 715/212, 715/215
• US Class Current: 715/212
• CPC Class Codes: G06F 40/18 of spreadsheets form-filling G06F40/174

Abstract

A computer-implemented system, method and computer readable medium to generate graphical visualizations corresponding to a data set populated in a web-based document, such as a spreadsheet. The spreadsheet is accessible in an interne or cloud-based system, and enables users to automatically create graphical visualizations or representations based on recommendations by a spreadsheet application. The graphical visualizations may be automatically ranked such that the system provides the recommendations to the user for display. Automatic ranking is accomplished, for example, by determining data types from identifying column type, differentiating column types, and extracting data sets having attributes corresponding to various graphical visualizations.

Claims

1. A computer-implemented method to generate a graphical visualization in a web-based document application, comprising:

identifying a data type for each column in a data table having a first set of data;

differentiating the data types between each of the columns in the data table;

determining a second data set derived from the first data set and corresponding to at least one graphical visualization with an expected data set in a plurality of graphical visualizations, and when the data type for a respective column is not relevant to the expected data set of the at least one graphical visualization, removing the respective column in the data table;

selecting a set of graphical visualizations comprised of each graphical visualization having the expected data set suitable for the second data set;

ranking, based on a criteria and the second data set, each graphical visualization in the set of graphical visualizations to graphically represent the second data set;

generating at least one graphical visualization from the set of graphical visualizations for display based on the ranking; and

determining each of the second data sets by;

rearranging at least one of the columns to correspond with a respective graphical visualization in the set of graphical visualizations, and

partitioning the columns in the data table to form separate partitioned second data sets, each set having columns with similar data types; and

examining the partitioned second data sets to determine respective data structures.

2. The computer-implemented method according to claim 1, wherein identification of the data type is determined by analyzing information from each of the columns in the data table, the extracted information including at least one of primitive types of column values, column labels, value formatting, known values and customized properties.

3. The computer-implemented method according to claim 1, further comprising:

determining whether each graphical visualization in the set of graphical visualizations corresponds to the data structure;

selecting each graphical visualization in the set of graphical visualizations that is representative of the data structure; and

discarding each graphical visualization in the set of graphical visualizations that is not representative of the data structure.

4. The computer-implemented method according to claim 3, further comprising:

applying the criteria to determine at least one of the compatibility and aesthetic attractiveness of each selected graphical visualization to a range of data in the partitioned second data sets;

determining a context for each selected graphical visualization in the set of graphical visualizations; and

ranking each selected graphical visualization in the set of graphical visualizations according to at least one of the criteria and context.

5. The computer-implemented method according to claim 4, wherein the context includes at least one of visualization popularity, previously used visualizations, visualization used in a same context, visualizations used for a same data set and visualizations generating insight.

6. The computer-implemented method according to claim 1, wherein the at least one generated graphical visualization is the highest ranking.

7. The computer-implemented method according to claim 1, wherein the web-based document application resides on a web-based server that communicates via a network with a client device.

8. The computer-implemented method according to claim 7, wherein the web-based document and graphical visualizations are displayed on the client device via a web page.

9. The computer-implemented method according to claim 7, wherein the web-based document is populated with the first data set acquired from one of a storage device, the client device and the web-based server.

10. The computer-implemented method according to claim 1, wherein the web-based document is a spreadsheet.

11. The computer-implemented method according to claim 10, further comprising:

acquiring the first data set into the data table from a database;

examining data from the first data set to determine a data structure; and

calculating a number of rows and columns in the data table which includes the first data set.

12. A non-transitory computer readable medium storing instructions to generate a graphical visualization in a web-based document application, the instructions when executed by a processor, comprising:

identifying a data type for each column in a data table having a first set of data;

differentiating the data types between each of the columns in the data table;

determining a second data set derived from the first data set and corresponding to at least one graphical visualization with an expected data set in a plurality of graphical visualizations, and when the data type for a respective column is not relevant to the expected data set of the at least one graphical visualization, removing the respective column in the data table;

selecting a set of graphical visualizations comprised of each graphical visualization having the expected data set suitable for the second data set;

ranking, based on a criteria and the second data set, each graphical visualization in the set of graphical visualizations to graphically represent the second data set;

generating at least one graphical visualization from the set of graphical visualizations for display based on the ranking; and

determining each of the second data sets by;

rearranging at least one of the columns to correspond with a respective graphical visualization in the set of graphical visualizations, and

partitioning the columns in the data table to form separate partitioned second data sets, each set having columns with similar data types; and

examining the partitioned second data sets to determine respective data structures.

13. The non-transitory computer readable medium according to claim 12, wherein identification of the data type is determined by analyzing information from each of the columns in the data table, the extracted information including at least one of primitive types of column values, column labels, value formatting, known values and customized properties.

14. The non-transitory computer readable medium according to claim 12, further comprising:

determining whether each graphical visualization in the set of graphical visualizations corresponds to the data structure;

selecting each graphical visualization in the set of graphical visualizations that is representative of the data structure; and

discarding each graphical visualization in the set of graphical visualizations that is not representative of the data structure.

15. The non-transitory computer readable medium according to claim 14, further comprising:

applying the criteria to determine at least one of the compatibility and aesthetic attractiveness of each selected graphical visualization to a range of data in the partitioned second data sets;

determining a context for each selected graphical visualization in the set of graphical visualizations; and

ranking each selected graphical visualization in the set of graphical visualizations according to at least one of the criteria and context.

16. The non-transitory computer readable medium according to claim 15, wherein the context includes at least one of visualization popularity, previously used visualizations, visualization used in a same context, visualizations used for a same data set and visualizations generating insight.

17. The non-transitory computer readable medium according to claim 12, wherein the at least one generated graphical visualization is the highest ranking.

18. The non-transitory computer readable medium according to claim 12, wherein the web-based document application resides on a web-based server that communicates via a network with a client device.

19. The non-transitory computer readable medium according to claim 18, wherein the web-based document and graphical visualizations are displayed on the client device via a web page.

20. The non-transitory computer readable medium according to claim 18, wherein the web-based document is populated with the first data set acquired from one of a storage device, the client device and the web-based server.

21. The non-transitory computer readable medium according to claim 12, wherein the web-based document is a spreadsheet.

22. A computer-implemented server to generate a graphical visualization in a web-based document application, comprising:

a memory storing an application; and

at least one processor executing the application to;

identify a data type for each column in a data table having a first set of data;

differentiate the data types between each of the columns in the data table;

determine a second data set derived from the first data set and corresponding to at least one graphical visualization with an expected data set in a plurality of graphical visualizations, and when the data type for a respective column is not relevant to the expected data set of the at least one graphical visualization, remove the respective column in the data table;

select a set of graphical visualizations comprised of each graphical visualization having the expected data set suitable for the second data set;

rank, based on criteria and the second data set, each graphical visualization in the set of graphical visualizations to graphically represent the second data set;

generate at least one graphical visualization from the set of graphical visualizations for display based on the ranking; and

determine each of the second data sets by;

rearranging at least one of the columns to correspond with a respective graphical visualization in the set of graphical visualizations, and

partitioning the columns in the data table to form separate partitioned second data sets, each set having columns with similar data types; and

examine the partitioned second data sets to determine respective data structures.

23. The computer-implemented server according to claim 22, wherein the server is connected to at least one client device via a network.

24. The computer-implemented server according to claim 23, wherein the spreadsheet and graphical visualizations are displayed on the client device via a web page.

25. The computer-implemented server according to claim 22, wherein the processor further executes the application to:

determine whether each graphical visualization in the set of graphical visualizations corresponds to the data structure;

select each graphical visualization in the set of graphical visualizations that is representative of the data structure; and

discard each graphical visualization in the set of graphical visualizations that is not representative of the data structure.

26. The computer-implements server according to claim 25, wherein the processor further executes the application to:

apply the criteria to determine at least one of the compatibility and aesthetic attractiveness of each selected graphical visualization to a range of data in the partitioned second data sets;

determine a context for each selected graphical visualization in the set of graphical visualizations; and

rank each selected graphical visualization in the set of graphical visualizations according to at least one of the criteria and context.