Local Neural Query Prediction for Unknown Large Data Sets
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Solution Overview
Problem
Existing database query solutions are inefficient for large and unknown data sets, as indexing and caching algorithms often fail to process such data effectively, leading to prolonged query times.
Innovation Solution
Implementing a method that uses a primary neural network to generate local approximations of query results by sending a model to a local machine, where a local neural network can predict user queries, reducing the need for direct database access and improving response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If indexing and caching algorithms are used to accelerate database queries, then query response time is improved for known data sets, but these algorithms fail to process large and unknown data sets effectively
Solution Approach 1:
The patent creates a neural network model that copies and learns from the database structure and data patterns. Instead of using traditional indexing algorithms that fail on unknown data, the neural network model is trained on the database and can generalize to query unknown or unseen data sets by applying learned patterns, thus providing adaptability while maintaining fast response times.
Solution Approach 2:
The patent transforms the database querying approach by changing from deterministic indexing algorithms to probabilistic neural network predictions. This parameter change allows the system to handle unknown data sets by using learned statistical patterns rather than requiring pre-defined indexes, thereby improving adaptability while maintaining query speed.
2Measurement precision
If a full database is accessed to ensure accurate query results, then result accuracy is maintained, but query execution time increases significantly
Solution Approach 1:
The patent performs preliminary action by training a neural network model on the database beforehand. This pre-training phase learns the data patterns, relationships, and structures so that during query execution, the system can provide accurate results quickly using the pre-learned model without accessing the entire database, thus reducing query time while maintaining accuracy.
Solution Approach 2:
The patent introduces a neural network model as an intermediary between the user query and the actual database. This intermediary has been trained to predict query results accurately, so it can provide fast approximations without requiring full database access, thereby reducing execution time while maintaining result accuracy through its learned predictions.
3Quantity of substance
If traditional database querying methods are used on large data sets, then complete data analysis is possible, but the process becomes increasingly complex and resource intensive
Solution Approach 1:
The patent extracts the essential patterns and structures from the large database into a compact neural network model. This extraction process captures the key relationships and data characteristics in a condensed form, allowing the system to work with large data sets by using the smaller model rather than processing the entire database directly, thus reducing complexity while maintaining analytical capability.
Data Source
AI summary
A system and method for providing local approximations of query results are provided. The method includes querying a primary neural network with at least one test query, wherein the at least one test query includes a real test result derived from executing the at least one training query on a data set; receiving from the primary neural network a predicted test result in response to the at least one test query; sending, based on the predicted test result, a model of a primary neural network to a local machine; and storing the model of a local neural network of the local machine, wherein the local neural network is configured to generate a prediction in response to a user query received by the local machine.