Neural Network Query Translation Table for Database Efficiency
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Solution Overview
Problem
Existing solutions for gaining insights from large and complex data sets are resource-intensive and inefficient, particularly when applied to vast amounts of data, as they often rely on indexing, caching, and database optimization methods that fall short in processing unknown data sets.
Innovation Solution
The method involves generating a translation table that maps query elements to vectors, allowing for reduced numerical representations of input query elements, which are then processed by an artificial neural network to generate results for database queries, thereby reducing data loss and improving query processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional database indexing and caching methods are used, then query processing speed is improved for known data sets, but the system becomes resource-intensive and ineffective for large unknown data sets
Solution Approach 1:
The patent transforms query elements from their original format into numerical vector representations, changing the parameter space in which queries are processed. This allows the neural network to operate on numerical data efficiently without requiring traditional indexing structures, reducing resource consumption while maintaining processing speed for both known and unknown data sets.
Solution Approach 2:
The patent replaces traditional database mechanical systems (indexing structures, caching mechanisms, query optimization algorithms) with an artificial neural network that processes queries through learned patterns. This substitution eliminates the need for pre-computed indexes and cache memory, reducing resource requirements while handling large unknown data sets effectively.
2Reliability
If traditional database methods are applied to vast amounts of data, then complete data processing is achieved, but query completion time increases to several minutes or hours
Solution Approach 1:
The patent performs preliminary training of the neural network on historical query data and patterns before actual query processing. During this preliminary phase, the system learns efficient representations and relationships in the data, enabling it to quickly process new queries without scanning entire data sets, thus reducing query completion time while maintaining processing completeness.
Solution Approach 2:
The patent introduces numerical vector representations as an intermediary between raw query elements and the neural network processing. This intermediary transformation enables the system to work with compressed, efficient representations of data, reducing the time required to process vast amounts of information while preserving the necessary information for complete and accurate query results.
3Productivity
If query elements are mapped to reduced numerical representations, then processing efficiency is improved, but data loss may occur
Solution Approach 1:
The patent employs feedback mechanisms during the training phase where the neural network learns to map query elements to numerical representations while minimizing information loss. The system continuously adjusts its mapping based on performance feedback, learning to preserve critical information in the reduced numerical form. This enables efficient processing while maintaining query accuracy.
Solution Approach 2:
The patent transforms query elements into numerical vector representations with optimized dimensions that balance compression efficiency with information preservation. By carefully selecting the parameter space and dimensionality of the numerical representations, the system achieves high processing efficiency while minimizing data loss through learned optimal mappings rather than arbitrary reductions.
Data Source
AI summary
A system and method for representing query elements in an artificial neural network. A method includes generating a translation table based on a plurality of query elements, wherein the translation table maps a plurality of vectors to the plurality of query elements, wherein each of the plurality of vectors is mapped to at least one query element of the plurality of query elements, wherein a first vector of the plurality of vectors is mapped to at least two query elements of the plurality of query elements; converting a plurality of input query elements into respective numerical representations using the translation table; and generating a result for a database query based on the numerical representations.