Automatic flagging of frequently accessed data objects for business application

• US 9,471,642 B2
• Filed: 10/10/2013
• Issued: 10/18/2016
• Est. Priority Date: 10/10/2013
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP SE
• Inventors: Zhang, Zhong
• Primary Examiner(s): Filipczyk, Marc
• Application Number:
  US14/051,321
• Publication Number:
  US 20150106383A1
• Time in Patent Office: 1,104 Days
• Field of Search: 707/732, 707/737, 707/748
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Embodiments relate to management of data accessed from a database. A usage tracking engine of an application layer overlying a database, may automatically track (e.g. with a time stamp) access to specific data objects by particular users. This automatic tracking may be based upon one or more of the following: frequency of access, recency of access, user geographic location, and user calendar events. Based upon this data, the tracking engine applies an algorithm to automatically identify those data objects meriting special handling for quick access (e.g. for placement in a “shelf” or other construct readily accessible to the user). A heuristic learning module may generate a data object ranking based upon the usage data, and communicate that ranking to the usage tracking engine to determine a data object'"'"'s eligibility for placement on the “shelf”. Such ranking may consider personalized settings, and/or a user'"'"'s past manual shelf placement of data object(s).

Claims

1. A method comprising:

providing a usage tracking engine in an in-memory database and in communication with usage data of a data object of the in-memory database;

causing the usage tracking engine to track access to the data object by a database user;

causing the usage tracking engine to receive a ranking of the data object from a heuristic learning module in the in-memory database that considers;

a geographic location of the database user,

a time stamp of access to the data object by the database user, and

prior manual placement of the data object in a shelf by the database user;

causing the usage tracking engine to check a personalization setting reflecting a number of data objects to be located on the shelf in an application layer accessible to the database user, the number based upon a storage capacity available to the database user; and

causing the usage tracking engine to displace the data object from the shelf based upon the ranking and the personalization setting.

2. The method of claim 1 wherein the usage information reflects a frequency of accessing the data object by the database user.

3. The method of claim 1 wherein the usage information reflects a recency of accessing the data object by the database user.

4. The method of claim 1 wherein the usage information is stored in the in-memory database.

5. The method of claim 1 wherein the ranking further reflects a calendar entry of the database user.

6. The method of claim 1 wherein the heuristic learning module further considers whether the database user changed the data object, wherein a change in the data object increases the ranking.

7. A computer system comprising:

a processor; and

a non-transitory computer readable medium having stored thereon one or more programs, which when executed by the processor, causes the processor to;

provide a usage tracking engine in an in-memory database and in communication with usage data of a data object of the in-memory database;

cause the usage tracking engine to track access to the data object by a database user;

cause the usage tracking engine to receive a ranking of the data object from a heuristic learning module in the in-memory database that considers;

a geographic location of the database user,

a time stamp of access to the data object by the database user, and

prior manual placement of the data object in a shelf by the database user;

cause the usage tracking engine to check a personalization setting reflecting a number of data objects to be located on the shelf in an application layer accessible to the database user, the number based upon a storage capacity available to the database user; and

cause the usage tracking engine to displace the data object from the shelf based upon the ranking and the personalization setting.

8. The computer system of claim 7 wherein the usage information reflects a frequency of accessing the data object by the database user.

9. The computer system of claim 7 wherein the usage information reflects a recency of accessing the data object by the database user.

10. The computer system of claim 7 wherein the usage information is stored in the in-memory database.

11. The computer system of claim 7 wherein the ranking further reflects a calendar entry of the database user.

12. The computer system of claim 7 wherein the heuristic learning module further considers whether the database user changed the data object, wherein a change in the data object increases the ranking.

13. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions for:

providing a usage tracking engine in an in-memory database and in communication with usage data of a data object of the in-memory database;

causing the usage tracking engine to track access to the data object by a database user;

causing the usage tracking engine to receive a ranking of the data object from a heuristic learning module in the in-memory database that considers,

a geographic location of the database user,

a time stamp of access to the data object by the database user, and

prior manual placement of the data object in a shelf by the database user;

causing the usage tracking engine to check a personalization setting reflecting a number of data objects to be located on the shelf in an application layer accessible to the database user, the number based upon a storage capacity available to the database user; and

causing the usage tracking engine to displace the data object from the shelf based upon the ranking and the personalization setting.

14. The non-transitory computer readable storage medium of claim 13 wherein the usage information reflects a frequency of accessing the data object by the database user.

15. The non-transitory computer readable storage medium of claim 13 wherein the usage information reflects a recency of accessing the data object by the database user.

16. The non-transitory computer readable storage medium of claim 13 wherein the usage information is stored in the in-memory database.

17. The non-transitory computer readable storage medium of claim 13 wherein the ranking further reflects a calendar entry of the database user.

18. The non-transitory computer readable storage medium of claim 13 wherein the heuristic learning module further considers whether the database user changed the data object, wherein a change in the data object increases the ranking.