Neural Network Data Element Weighting to Reduce Server Load
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Solution Overview
Problem
Conventional networked computing systems face inefficiencies in managing and accessing data elements across multiple application servers due to varying computational resource requirements and usefulness levels, necessitating a system to improve the efficacy of data elements within neural networks.
Innovation Solution
A system that identifies data elements, collects usage values, calculates computational weights, and generates unique hash values and connection strings for critical data elements, reducing resource usage and optimizing storage and access processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data elements are stored and maintained across multiple application servers, then data availability and accessibility are improved, but computational resource consumption increases
Solution Approach 1:
The patent extracts critical data elements from distributed application servers and consolidates them into a centralized neural network structure. The system identifies, extracts, and isolates high-value data elements that are frequently accessed across multiple servers, moving them to a dedicated neural network for efficient management and reducing the burden on individual application servers.
Solution Approach 2:
The neural network serves multiple functions simultaneously: it stores data elements, calculates their efficacy weights, generates hash values for identification, and provides centralized access points for multiple application servers. This multi-functional approach replaces multiple separate storage and management systems with a single universal platform.
2Stability of the object's composition
If all data elements are maintained with equal computational resources, then data consistency is improved, but system efficiency deteriorates
Solution Approach 1:
The patent applies local quality by assigning different computational resource levels to different data elements based on their individual efficacy weights. Instead of uniform resource allocation, the system calculates a weight for each data element reflecting its importance and usage patterns, then allocates storage, maintenance, and access resources proportionally to these weights.
Solution Approach 2:
The system dynamically changes the parameter of computational resource allocation based on the calculated efficacy weight of each data element. High-weight elements receive more resources for consistent maintenance and faster access, while low-weight elements receive minimal resources, allowing the system to adapt resource distribution to actual data value and usage patterns.
3Speed
If data elements are frequently accessed across multiple servers, then service responsiveness is improved, but network traffic increases
Solution Approach 1:
The patent merges multiple data access operations into a single centralized neural network access point. Instead of allowing application servers to directly access data elements across the network, all access requests are routed through the neural network, which consolidates traffic and eliminates redundant data transmissions between servers.
Solution Approach 2:
The neural network acts as an intermediary between application servers and data elements. It receives access requests from multiple servers, retrieves the required data elements from its centralized storage, and returns them to the requesting servers. This intermediary role eliminates direct peer-to-peer data access and reduces overall network traffic.
Data Source
AI summary
In conventional networked computing systems, each system may have many atomic values shared across multiple application servers. Each atomic value may require different levels of computational resources to store, maintain, and access, and may provide a different level of usefulness within the computing system. As such, a need exists for a system of improving the efficacy of each data element with a neural network or networked computing system. The system provided herein solves with problem by identifying each data element within a computing system; crawling multiple application servers to collect usage values for each data element; calculating a computational weightage of each data elements, identifying critical values; and creating hash values for each critical value.