Natural Language Query Processing Pipeline with Multi-Stage Filtering
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Solution Overview
Problem
The increasing complexity and cost of managing and storing large volumes of data across various storage systems make it challenging for organizations to efficiently access and analyze data, especially when users need to interact with diverse data sets for business intelligence or analytics without knowing the specific storage systems or data sets available.
Innovation Solution
Implementing a natural language query processing system that translates human language queries into executable queries across multiple data storage systems, using multiple stage filtering and intermediate representation generation to identify relevant data sets and metadata, allowing users to query data without specifying the storage systems, and generating visualizations from the results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored across multiple different storage systems to optimize performance and analysis benefits, then data accessibility and analysis capability are improved, but system complexity and operational cost increase
Solution Approach 1:
The patent introduces a natural language query processing system as an intermediary layer between users and multiple storage systems. This mediator translates natural language queries into appropriate queries for different storage systems, eliminating the need for users to directly manage or understand the complexity of multiple storage systems while maintaining data accessibility across all of them.
Solution Approach 2:
The query processing system is designed to work universally across multiple different storage systems (relational databases, data lakes, data warehouses, etc.). A single natural language processing interface can query any of these diverse systems, making the system multi-functional and eliminating the need for separate interfaces for each storage system.
2Adaptability or versatility
If data is stored across multiple different storage systems to optimize performance and analysis benefits, then data accessibility and analysis capability are improved, but operational cost increases
Solution Approach 1:
The system uses multi-stage filtering to process queries efficiently, applying filters at different stages to progressively narrow down the search space. This partial action approach avoids unnecessarily querying all storage systems, reducing operational costs while maintaining the ability to access data across multiple systems when needed.
Solution Approach 2:
The query processing is divided into multiple stages with different filtering mechanisms. Each stage handles a portion of the query processing work, segmenting the overall task to reduce the computational burden on any single component and lowering operational costs while maintaining comprehensive data accessibility.
3Measurement precision
If users need to specify storage systems and data sets for queries, then query precision is improved, but ease of operation decreases
Solution Approach 1:
The system automatically determines which storage systems and data sets to query based on the natural language input. The query processing system performs self-service by autonomously analyzing the query intent, identifying relevant data sources, and executing appropriate queries without requiring users to manually specify technical details about storage systems.
Solution Approach 2:
The natural language processing system acts as an intermediary that translates user intent into precise queries. It bridges the gap between simple natural language input and the complex requirements of querying specific storage systems, maintaining query precision while improving ease of operation.
4Adaptability or versatility
If comprehensive data sets are made available for querying, then data discovery capability is improved, but system complexity increases
Solution Approach 1:
The query processing is segmented into multiple stages with different filtering mechanisms. Each stage handles a specific aspect of query processing, dividing the complex task of querying comprehensive data sets into manageable segments that reduce overall processing complexity while maintaining data discovery capability.
Solution Approach 2:
The system applies filtering at multiple stages to progressively narrow down the search space. By performing partial actions at each stage rather than processing all data at once, the system maintains the ability to discover comprehensive data sets while reducing processing complexity at any given moment.
Data Source
AI summary
Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.