Smart Cache Learning for Portal Navigation Nodes

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Solution Overview

Problem

Current portal navigation node caching mechanisms lack control over caching performance and data storage usage, as they cache entire navigation nodes regardless of property usage, leading to inefficient use of resources.

Innovation Solution

Implement a learning process to analyze navigation requests, record requested properties, calculate variance, and determine a threshold to selectively cache only relevant properties, adjusting caching behavior based on usage patterns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If entire navigation nodes are cached regardless of property usage, then runtime performance is improved, but data storage space is wasted

Engineering Contradiction:

Improveruntime performanceVSAvoiddata storage space

Core Design Contradiction:

SpeedVSQuantity of substance

Solution Approach 1:

The navigation node is segmented into individual properties, and only the frequently accessed properties are cached separately rather than caching the entire navigation node. This allows selective caching of specific properties (e.g., title, description, icon) based on their access frequency, thereby reducing storage space while maintaining runtime performance for the most commonly used properties.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different properties of the navigation node are treated differently based on their local characteristics (access frequency). Properties with high access frequency are cached, while properties with low access frequency are not cached. This local quality approach ensures that storage resources are allocated efficiently to only those properties that provide the most value for runtime performance.

Inventive Principle:

Principle #3Local quality

2Speed

If entire navigation nodes are cached regardless of property usage, then runtime performance is improved, but caching control is reduced

Engineering Contradiction:

Improveruntime performanceVSAvoidcaching control

Core Design Contradiction:

SpeedVSAdaptability or versatility

Solution Approach 1:

The system implements feedback by monitoring property access frequency and using this information to dynamically adjust caching behavior. The caching mechanism learns from usage patterns and adapts by caching properties that are frequently accessed while avoiding caching of rarely accessed properties, thereby providing organizations with control over the caching process to optimize both performance and resource usage.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The caching strategy transitions from a static approach (caching entire navigation nodes) to a dynamic approach where the caching behavior adapts based on observed usage patterns. The system dynamically determines which properties to cache based on their access frequency, allowing the caching control to be flexible and responsive to changing organizational needs.

Inventive Principle:

Principle #15Dynamics

3Quantity of substance

If selective property caching is implemented, then data storage space is optimized, but caching mechanism complexity increases

Engineering Contradiction:

Improvedata storage spaceVSAvoidcaching mechanism complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The caching mechanism serves itself by automatically monitoring property access frequency and making decisions about which properties to cache without requiring complex external control systems. The system self-adjusts by learning from usage patterns and automatically optimizing the cache contents, thereby reducing storage space without proportionally increasing mechanism complexity.

Inventive Principle:

Principle #25Self-service

4Productivity

If learning process is used to analyze navigation requests, then caching optimization is achieved, but processing time during learning period increases

Engineering Contradiction:

Improvecaching optimizationVSAvoidprocessing time during learning

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The learning process performs preliminary analysis of navigation requests to identify which properties are frequently accessed. By conducting this analysis in advance and building up the optimized cache gradually, the system prepares the caching mechanism to achieve optimal performance without incurring continuous processing overhead during normal operation. The initial time investment during the learning period enables long-term efficiency gains.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS8756292B2Smart cache learning mechanism in enterprise portal navigation

Publication Date: 2014.06.17 SAP PORTALS ISRAEL

AI summary

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.