Phrase Translation for Low-Latency Database Analysis
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Solution Overview
Problem
Existing database analytic tools are inefficient, costly, and require substantial configuration and training, making it difficult for businesses to effectively analyze and utilize large volumes of data stored in complex database systems.
Innovation Solution
A low-latency database analysis system that implements phrase translation, using localization definition data to generate locale-specific indices and finite state machines, allowing for efficient translation and processing of data queries across different locales and languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database analytic tools are used, then data analysis capability is provided, but the system is inefficient, costly, and requires substantial configuration and training
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the database system. This intermediary translates user-friendly natural language queries into database-specific query languages, eliminating the need for users to directly configure complex database tools or undergo extensive training. The translation layer acts as a mediator that handles the complexity internally while presenting a simplified interface to users.
Solution Approach 2:
The system implements self-service through automatic query translation and optimization. The database analysis system automatically translates natural language input into optimized database queries without requiring manual configuration or user intervention in the complex translation process. The system serves itself by handling the complexity of query optimization and translation internally, freeing users from configuration tasks.
2Quantity of substance
If complex database systems store large volumes of data, then data storage capacity is improved, but analysis speed and accessibility decrease
Solution Approach 1:
The system performs preliminary actions by pre-compiling and optimizing translation rules and query templates before actual data analysis occurs. The natural language to database query translation framework is prepared in advance with predefined patterns and optimization strategies, allowing rapid translation and execution during actual analysis without performing complex optimization in real-time, thus maintaining high speed even with large data volumes.
3Ease of operation
If existing database tools are used, then data querying is possible, but substantial configuration and training are required
Solution Approach 1:
The natural language processing system serves as an intermediary that absorbs all configuration complexity internally. Users simply input natural language queries without needing to configure anything, while the intermediary handles the complex translation, optimization, and adaptation to different database systems automatically. This completely decouples user simplicity from system complexity.
4Productivity
If traditional analytic tools are deployed, then data analysis functionality is provided, but cost of utilization increases
Solution Approach 1:
The system uses copying by creating and reusing optimized query templates and translation rules across multiple queries and users. Instead of performing full optimization for each individual query, the system copies and adapts pre-optimized templates, significantly reducing computational overhead and resource consumption. This template-based approach allows the system to serve multiple users and queries efficiently without repeating expensive optimization processes.
Data Source
AI summary
Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency database analysis system, and outputting the results data for presentation to a user.