Ontological Content Filtering for Bandwidth-Limited Delivery
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Solution Overview
Problem
The existing internet infrastructure is unable to meet the increasing demand for fast delivery of content, particularly video, due to bandwidth limitations, with existing solutions primarily focusing on pre-designated content like icons and ads rather than the actual content users desire.
Innovation Solution
A system that utilizes local content storage and a network appliance within multi-dwelling units, connected to a central processing cloud, to identify and cache content likely to be desired by users based on usage patterns and trend data, ensuring immediate availability through high-speed local delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If content is delivered through conventional internet infrastructure, then bandwidth limitations are encountered, but delivery speed is insufficient for user demand
Solution Approach 1:
The system performs preliminary actions by caching content locally at edge servers before users request it. The content delivery system anticipates user需求的 and pre-positions content in local caches, eliminating the need to transfer content over the constrained internet infrastructure when users access it.
Solution Approach 2:
The system segments the content delivery architecture into multiple levels: edge servers with local caches, regional servers, and central content sources. This segmentation allows frequently accessed content to be served from local edge caches, reducing the load on the main internet infrastructure and improving delivery speed for local users.
2Adaptability or versatility
If pre-designated content like icons and ads is cached at edge servers, then some content delivery is improved, but actual desired content is not included
Solution Approach 1:
The system implements feedback mechanisms where user access patterns, preferences, and behavior are continuously monitored and analyzed. This feedback information is used to dynamically update cache content at edge servers, ensuring that the most desired content by local users is pre-cached and readily available.
Solution Approach 2:
The content caching system is dynamic rather than static. Edge servers continuously adapt their cache content based on real-time user behavior analysis, seasonal trends, and changing user preferences. This dynamic adaptation ensures that the cached content always reflects current user demands rather than pre-designated fixed content.
3Speed
If content is cached locally at edge servers, then delivery speed is improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including edge servers with integrated cache management systems and intelligent proxies. These intermediaries handle the complexity of content selection, caching, and delivery optimization, shielding end users from the underlying system complexity while delivering fast content access.
Solution Approach 2:
Edge servers are equipped with autonomous cache management capabilities that automatically analyze user patterns, select appropriate content to cache, and manage cache updates without requiring manual intervention. This self-service approach reduces operational complexity while maintaining high performance.
Data Source
AI summary
System for ontological evaluation and filtering of digital content evaluates metadata associated with content available from an original content server. The metadata is filtered and evaluated by a processing cluster to develop correlation among content for the formation of content “channels”.