Local Neural Query Approximation for Fast Database Responses
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Solution Overview
Problem
Existing database query solutions, such as indexing and caching, are inefficient for large and unknown data sets, leading to prolonged query times, as they are not programmed to process all types of data effectively.
Innovation Solution
Implementing a method that uses a primary neural network to generate local approximations of query results by training on test queries and sending a model to a local machine, allowing the local neural network to predict user queries, thereby reducing the need for direct database access and improving query response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional database optimization methods (indexing, caching) are used, then query processing is simplified for known data sets, but query times become excessively long for large and unknown data sets
Solution Approach 1:
The system performs preliminary training of neural networks on historical query data and stores trained models in advance. When a query arrives, the pre-trained neural network can immediately process it without requiring traditional database scanning, thus resolving the contradiction between fast response and handling unknown data patterns.
Solution Approach 2:
The invention creates a copy of the database's query-processing capability in the form of a neural network model. This copied model can approximate query results without accessing the actual database, enabling fast responses for unknown data sets while maintaining compatibility with the original database structure.
2Measurement precision
If neural networks are trained on entire data sets to improve accuracy, then computational resources and training time increase significantly
Solution Approach 1:
The training process is segmented into multiple stages: initial training on a subset of data, followed by iterative refinement on additional data only when accuracy improvements are needed. This segmented approach reduces overall computational resource consumption while maintaining sufficient accuracy for practical applications.
Solution Approach 2:
The system uses partial action by training on subsets of data rather than complete data sets, and employs excessive action by iteratively adding more training data only when the accuracy threshold is not met, thus optimizing the balance between accuracy and resource consumption.
3Loss of time
If local neural networks are deployed on client machines, then query response time improves, but device complexity and memory requirements increase
Solution Approach 1:
Instead of deploying complex, large-scale neural networks on client machines, the system uses simplified, lightweight neural network models that require minimal memory and processing power. These simplified models provide sufficient accuracy for approximate query results while being compatible with standard client hardware resources.
Data Source
AI summary
A system and method for improving training of a recurrent neural network (RNN) to provide a response to a table-based database query is presented. The method includes: receiving a plurality of query pairs, each including a database query and a response, the response generated by executing the database query on a database; detecting a variable in each query; determining a variance of the variable; generating a subset of potential values for the detected variable based on the determined variance, wherein each potential value is different from the response of each query pair; generating a plurality of training queries, each training query based on a database query of a query pair of the plurality of query pairs and a corresponding potential value from the first subset; executing each training query to generate a training response; and training the RNN based on the plurality of training queries and a corresponding training response.