Dynamic Application Catalog Sorting by Usage Patterns
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Solution Overview
Problem
Users waste time searching for applications and waiting for them to load in their system application catalog, and virtualized or managed computing environments experience network bottlenecks and server lag due to simultaneous user requests, particularly at the start of the business day or after a lunch break.
Innovation Solution
The system optimizes application and notification delivery by using historical usage data to prioritize and pre-load applications based on user preferences and habits, ensuring that frequently used applications are easily accessible and reducing loading times, while also managing system resources to minimize bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If applications are displayed in traditional catalog order, then the application catalog structure is simple, but users waste time searching for applications
Solution Approach 1:
The application catalog transitions from a static alphabetical or category-based arrangement to a dynamic ordering system that automatically repositions applications based on real-time usage patterns. Frequently used applications rise to the top of the catalog, while less used ones move down, creating a living structure that adapts to user behavior without manual intervention.
Solution Approach 2:
The system automatically monitors and analyzes user application usage patterns, then autonomously reorganizes the application catalog based on detected preferences. This self-service mechanism eliminates the need for manual catalog curation or user configuration, as the system learns and adapts to individual user behaviors automatically.
2Loss of time
If applications are pre-loaded based on usage patterns, then user waiting time is reduced, but system resources are consumed
Solution Approach 1:
The system proactively loads applications into memory before users request them, based on predictive analysis of usage patterns. By anticipating which applications will be needed next and pre-loading them during off-peak moments, the system eliminates user-perceived waiting time while smoothing out resource consumption across different time periods.
Solution Approach 2:
Rather than pre-loading all possible applications, the system selectively pre-loads only those applications predicted to be used within a specific time window. This partial action approach balances resource consumption with user benefit, loading enough applications to satisfy near-term needs without exhausting system resources.
3Reliability
If multiple users request applications simultaneously, then application availability is ensured, but network bottlenecks and server lag occur
Solution Approach 1:
The system pre-loads applications onto local client devices based on collective usage patterns across the organization. By caching frequently accessed applications locally before peak demand periods, the system reduces network traffic during simultaneous access events, eliminating network bottlenecks while ensuring immediate application availability.
Solution Approach 2:
The system introduces local client-side caches and predictive pre-loading mechanisms as intermediaries between the central application server and users. This intermediary layer handles local application delivery, reducing the load on network infrastructure and server resources during peak simultaneous access periods.
Data Source
AI summary
System and methods discussed for automatically optimizing application and notification delivery based on user preferences and historical application usage. Applications that a user is likely to want to use at the present time or in the near future are displayed in an organizationally distinct way in an application catalog so they are easy to find and are pre-loaded on an application delivery server so they are available with minimal system lag caused by application loading processes. Application notifications are also optimized such that notifications that are likely to be relevant to users at the current time are identified and presented to them in an organizationally distinct way.