SMART CACHE LEARNING MECHANISM IN ENTERPRISE PORTAL NAVIGATION

• US 20130227112A1
• Filed: 02/24/2012
• Published: 08/29/2013
• Est. Priority Date: 02/24/2012
• Status: Active Grant

• Current Assignee: Sap Portals Israel Ltd (SAP SE)
• Original Assignee: Sap Portals Israel Ltd (SAP SE)
• Inventors: Lehmann, Doron, Barak, Nimrod
• Granted Patent: US 8,756,292 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06F 16/9574 of access to content, e.g. by caching

Abstract

The disclosure generally describes computer-implemented methods, software, and systems for optimizing portal navigation node caching. A computer-implemented method includes starting a learning process to analyze at least one navigation request to at least one navigation node, wherein the navigation request requests at least one property of the at least one navigation node, examining properties of the at least one navigation node, recording to a property list at least one requested navigation node property of the at least one navigation node, calculating, using at least one computer, a variance between the at least one navigation node property recorded to the property list, and determining whether the calculated variance is above a threshold.

Claims

1. A method, comprising:

starting a learning process to analyze at least one navigation request to at least one navigation node, wherein the navigation request requests at least one property of the at least one navigation node;

examining properties of the at least one navigation node;

recording to a property list at least one requested navigation node property of the at least one navigation node;

calculating, using at least one computer, a variance between the at least one navigation node property recorded to the property list; and

determining whether the calculated variance is above a threshold.

2. The method of claim 1, wherein the at least one navigation request is received by an enterprise portal.

3. The method of claim 1, further comprising recording additional data associated with the at least one requested navigation node property.

4. The method of claim 1, wherein the variance is calculated using the difference, as a function of time, between a determined top-most number of navigation node properties and a determined bottom-most number of navigation node properties recorded to the property list.

5. The method of claim 1, further comprising stopping the learning process upon the determination the calculated variance exceeds the threshold and recording a last hit/miss ratio of a cache.

6. The method of claim 5, further comprising generating a new property list.

7. The method of claim 6, further comprising monitoring the hit/miss ratio of the cache, wherein the cache caches the navigation node properties as indicated in the new property list.

8. The method of claim 7, further comprising determining whether the monitored miss ratio of the cache exceeds the miss ratio of the last recorded hit/miss ratio.

9. The method of claim 8, further comprising restarting the learning process.

10. A computer-program product, the computer program product comprising computer-readable instructions embodied on tangible, non-transitory media, the instructions operable when executed to perform operations comprising:

starting a learning process to analyze at least one navigation request to at least one navigation node, wherein the navigation request requests at least one property of the at least one navigation node;

examining properties of the at least one navigation node;

recording to a property list at least one requested navigation node property of the at least one navigation node;

calculating, using at least one computer, a variance between the at least one navigation node property recorded to the property list; and

determining whether the calculated variance is above a threshold.

11. The computer-program product of claim 10, wherein the at least one navigation request is received by an enterprise portal.

12. The computer-program product of claim 10, further comprising recording additional data associated with the at least one requested navigation node property.

13. The computer-program product of claim 10, wherein the variance is calculated using the difference, as a function of time, between a determined top-most number of navigation node properties and a determined bottom-most number of navigation node properties recorded to the property list.

14. The computer-program product of claim 10, further comprising stopping the learning process upon the determination the calculated variance exceeds the threshold and recording a last hit/miss ratio of a cache.

15. The computer-program product of claim 14, further comprising generating a new property list.

16. The computer-program product of claim 15, further comprising monitoring the hit/miss ratio of the cache, wherein the cache caches the navigation node properties as indicated in the new property list.

17. The computer-program product of claim 16, further comprising determining whether the monitored miss ratio of the cache exceeds the miss ratio of the last recorded hit/miss ratio.

18. The computer-program product of claim 17, further comprising restarting the learning process.

19. A system, comprising:

memory operable to store at least one navigation node; and

at least one hardware processor interoperably coupled to the memory and operable to;

start a learning process to analyze at least one navigation request to at least one navigation node, wherein the navigation request requests at least one property of the at least one navigation node;

examine properties of the at least one navigation node;

record to a property list at least one requested navigation node property of the at least one navigation node;

calculate, using at least one computer, a variance between the at least one navigation node property recorded to the property list; and

determine whether the calculated variance is above a threshold.

20. The system of claim 19, wherein the at least one navigation request is received by an enterprise portal.

21. The system of claim 19, further comprising recording additional data associated with the at least one requested navigation node property.

22. The system of claim 19, wherein the variance is calculated using the difference, as a function of time, between a determined top-most number of navigation node properties and a determined bottom-most number of navigation node properties recorded to the property list.

23. The system of claim 19, further comprising stopping the learning process upon the determination the calculated variance exceeds the threshold and recording a last hit/miss ratio of a cache.

24. The system of claim 23, further comprising generating a new property list.

25. The computer-program product of claim 24, further comprising monitoring the hit/miss ratio of the cache, wherein the cache caches the navigation node properties as indicated in the new property list.

26. The system of claim 25, further comprising determining whether the monitored miss ratio of the cache exceeds the miss ratio of the last recorded hit/miss ratio.

27. The system of claim 26, further comprising restarting the learning process.