DISPLAYING DIFFERENCES BETWEEN DIFFERENT DATA SETS OF A PROCESS

• US 20180225027A1
• Filed: 03/31/2018
• Published: 08/09/2018
• Est. Priority Date: 12/04/2011
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Cooke, Richard Martin, Sengupta, Arijit, Stronger, Brad A., Chronis, Griffin
• Granted Patent: US 10,802,687 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/41 Interactive pattern learning with a human teacher
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.

Claims

1. A method for comparing differences in an outcome between a data set A for a process and a different data set B for the same process, the method comprising a computer system automatically performing the following:

processing each of the data sets A and B, each data set containing observations of the process, the observations expressed as values for a plurality of variables and for the outcome, wherein processing each data set comprises estimating impacts of different variable combinations on the outcome based on (a) the behaviors of the variable combinations with respect to the outcome in that data set, and (b) populations of the variable combinations in that data set, the variable combinations defined by values for one or more of the variables; and

displaying a pair of graphs for the data sets A and B, one graph of the pair showing the estimated impact on the outcome due to different variable combinations for data set A and the other graph of the pair showing the estimated impact on the outcome due to different variable combinations for data set B, wherein both graphs in the pair show the estimated impacts due to the same variable combinations and in a same format to facilitate a direct comparison of the two graphs in the pair.

2. The method of claim 1, wherein the pair of graphs are displayed simultaneously in a user interface screen.

3. The method of claim 1, wherein the pair of graphs are displayed sequentially in a user interface screen.

4. The method of claim 1, wherein displaying the pair of graphs comprises displaying thumbnails of the pair of graphs in a secondary pane of a user interface screen.

5. The method of claim 4, wherein one of the pair of graphs is also displayed in a primary pane of the user interface screen.

6. The method of claim 5, wherein the thumbnails are used to select which graph is displayed in the primary pane of the user interface screen.

7. The method of claim 4, wherein the thumbnails further include labels describing the differences between thumbnails.

8. The method of claim 1, wherein data set B is a subset of data set A.

9. The method of claim 1, wherein data set A contains observations of the process before a certain time, and data set B contains additional observations of the process after the certain time.

10. The method of claim 1, wherein at least 80% of the observations in data set B are also in data set A.

11. The method of claim 1, wherein data sets A and B are both subsets of a common data set.

12. The method of claim 1, wherein the display of the pair of graphs in a user interface screen depend on a user'"'"'s interaction with the user interface screen.

13. The method of claim 1, wherein the outcome is derived from one or more observed variables.

14. The method of claim 1, wherein the outcome is selected from a group consisting of:

revenue, average revenue, profit, number of units sold, cost, delay, duration, and risk.

15. The method of claim 1, wherein processing the data sets comprises determining behaviors for all variable combinations of up to 3 variables for which the data set contains a statistically meaningful number of observations.

16. The method of claim 1, wherein processing the data sets comprises determining behaviors for all variable combinations of up to 2 variables for which the data set contains at least M observations, where M is an integer greater than or equal to 25.

17. The method of claim 1, wherein processing the data sets comprises determining behaviors for at least 1000 variable combinations.

18. The method of claim 1, wherein each data set comprises at least 100,000 observations.

19. The method of claim 1, wherein the estimated impact is an estimated impact on deviations from a norm of the data set.

20. The method of claim 1, wherein the estimated impact is an estimated impact on deviations for the outcome.