Neural Network Query Training Sets for Fast Unknown Data Retrieval
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Solution Overview
Problem
Existing database query solutions struggle to provide timely and accurate results, especially with large and unknown data sets, as indexing and caching algorithms often fail to process such data effectively.
Innovation Solution
A method and system utilizing neural networks to generate training sets based on query pairs, determining variable elements and variances, and generating training sets to approximate query results, which can be executed on user nodes or servers to provide faster and more accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional database indexing and caching methods are used, then query processing is simplified for known data sets, but query response time becomes unacceptably long for large and unknown data sets
Solution Approach 1:
The system performs preliminary actions by training neural networks in advance on representative data samples. The neural networks are pre-trained to learn patterns and relationships in the data, enabling them to quickly approximate query results without executing full database queries when unknown or large data sets are encountered.
Solution Approach 2:
The patent introduces neural networks as intermediary components between the query system and the database. These neural networks act as mediators that can rapidly approximate query results for unknown data sets, bridging the gap between traditional database methods and the need for fast responses on large, unfamiliar data.
2Measurement precision
If more data is stored and processed to improve query accuracy, then measurement precision improves, but device complexity and resource requirements increase
Solution Approach 1:
The system creates simplified copies of data patterns through neural network training. Instead of storing and processing all raw data, the neural networks learn and store compressed representations of data patterns, relationships, and trends. This allows accurate query approximation without the complexity of managing vast amounts of original data.
Solution Approach 2:
The patent transforms the problem from storing and processing raw data parameters to storing and processing learned pattern parameters. The neural networks convert complex data relationships into simplified parameter representations that can be quickly queried, maintaining accuracy while reducing system complexity.
Data Source
AI summary
A system and method for generating training sets for training neural networks. The method includes receiving a plurality of query pairs, wherein each of the plurality of query pairs includes a query and a real result previously determined for the query; determining at least one variable element of each query in the plurality of received query pairs; determining a variance for the at least determined variable element of each query in the plurality of received query pairs; and generating a training set based on the determined variable element, the determined variance, and the previously determined real result.