SMART DEFAULTS FOR DATA VISUALIZATIONS

• US 20120023101A1
• Filed: 07/21/2010
• Published: 01/26/2012
• Est. Priority Date: 07/21/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Heimendinger, Scott, Baumgartner, Patrick J., Burns, Jason
• Granted Patent: US 8,825,649 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/2457 with adaptation to user needs
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/953 Querying, e.g. by the use of web search engines
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

Smart defaults are provided for data visualization by creating a default layout of rows, columns, filters, and comparable elements that improve a user'"'"'s experience in finding relevant answers within the data. Usage history of the ways that users look at data in various data sources, user specific information, and inferred relationships between a current user and similar users are used to determine elements relevant to visualization of data for a particular user such that the visualization process may be automatically started, and a relevance model is formed/adjusted based on these factors. Queries may also be executed in a preemptive fashion based on the relevance model and results provided to a requesting user more rapidly enhancing user experience with networked data visualization.

Claims

1. A method executed at least in part in a computing device for providing default layout elements in data visualization, the method comprising:

receiving a user connection indication;

automatically retrieving user and usage information associated with the connecting user;

determining relevant layout elements based on the user and usage information; and

presenting a data visualization user interface employing the relevant layout elements.

2. The method of claim 1, further comprising:

executing a query; and

presenting results of the query employing the relevant layout elements.

3. The method of claim 1, wherein the layout elements include at least one from a set of:

a row definition, a column definition, a filter definition, a measure definition, and a formatting element definition.

4. The method of claim 1, wherein the user information includes at least one from a set of:

a user profile, a user title, a user position within an organization, an organizational profile, and an inferred relationship between the connecting user and at least one similar user.

5. The method of claim 1, wherein the usage information includes at least one from a set of:

a query history, a peer usage, at least one defined data source, aggregation methods for past queries, and a date and time of past query requests.

6. The method of claim 1, further comprising:

providing privacy protection measures that include at least one from a set of;

enabling the connecting user to opt out from having their personal information stored, associating the connecting user with an anonymous identifier, providing password protection for the user information, and deleting the user information after a predefined period.

7. The method of claim 1, further comprising inferring the layout elements from a past query, wherein the past query is broken into components and the components stored.

8. The method of claim 7, wherein the stored components of the past query include at least one from a set of:

a row, a column, a filter, and a measure.

9. The method of claim 7, wherein the layout elements are inferred from a default component for components not explicitly included in the past query.

10. A computing device for executing a data visualization application, the computing device comprising:

a memory;

a processor coupled to the memory, the processor executing the data visualization application, wherein the data visualization application is configured to;

receive a user connection indication;

automatically retrieve user and usage information associated with the connecting user;

provide the user and usage information to a relevance module, wherein the relevance module is configured to determine relevant layout elements based on the user and usage information;

receive the relevant layout elements from the relevance module;

receive a query request from the connecting user;

execute the requested query; and

visualize results of the requested query employing the relevant layout elements.

11. The computing device of claim 10, wherein the relevance module is further configured to execute a parameterized relevance algorithm adapted to receive the user and usage information as input and provide the relevant layout elements as output.

12. The computing device of claim 11, wherein the relevance algorithm returns a fully-constructed query to be executed by the data visualization application against a selected data source.

13. The computing device of claim 11, wherein the relevance algorithm returns component values for a query such that the query is constructed by the data visualization application to be executed against a selected data source.

14. The computing device of claim 11, wherein the relevance algorithm is one of a neural network algorithm, a Bayesian tree algorithm, a collaborative filtering algorithm, a data mining algorithm, and a machine learning algorithm.

15. The computing device of claim 11, wherein the data visualization application is further configured to enable the connecting user to modify at least one of the relevance algorithm and one or more relative weights associated with input parameters for the relevance algorithm in order to optimize an accuracy of the relevance module.

16. The computing device of claim 10, wherein the relevance module is further configured to retrieve data from an external data source to infer a relationship between the connecting user and at least one similar user.

17. The computing device of claim 10, wherein the query results are visualized as one of:

a two-dimensional table, a pivot table, a multi-dimensional table, a chart, and a diagram.

18. A computer-readable storage medium having instructions stored thereon for providing default layout elements in data visualization, the instructions comprising:

receiving a user connection indication;

automatically retrieving user and usage information associated with the connecting user;

providing the user and usage information as input to a relevance model executing a parameterized relevance algorithm adapted to return one of;

a fully-constructed query to be executed by a data visualization application against a selected data source and component values for a query such that the query is constructed by the data visualization application to be executed against a selected data source;

receiving the relevant layout elements from the relevance model;

receiving a query request from the connecting user;

executing the requested query; and

visualizing results of the requested query employing the relevant layout elements.

19. The computer-readable storage medium of claim 18, wherein the relevance model is configured to take into consideration a popularity of layout elements among similar users, a time decay, and a clustering of similar users, and wherein the relevance model is parameterized to adjust a relative importance of the inputs to the relevance model.

20. The computer-readable storage medium of claim 18, wherein the instructions further include:

surfacing one of a “

popular”

view and a “

users like you frequently look at”

view data set based on the relevance model feedback.