Natural Language Query System for Heterogeneous Data Sources
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Solution Overview
Problem
Current data analysis processes across heterogeneous data sources are complex, time-consuming, and require technical expertise, leading to delays and inefficiencies, as well as a knowledge gap between domain experts and technical users who lack domain knowledge.
Innovation Solution
An analysis system that connects to multiple data sources, retrieves metadata, and processes natural language questions by generating execution plans, ranking relevant data assets, and providing step-by-step instructions for data access and processing, eliminating the need for ETL processes and bridging the knowledge gap between technical and domain experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data analysis processes are used across heterogeneous data sources, then data can be analyzed, but the process becomes complex and time-consuming requiring multiple stages including data discovery, import, subset determination, and analysis validation
Solution Approach 1:
The system performs preliminary actions by automatically discovering data sources, extracting metadata, and generating executable queries before the user actually needs the analysis. The patent implements data discovery and metadata extraction in advance, creating a prepared state where queries can be executed immediately when needed, eliminating the multi-stage manual process that previously took weeks or months.
Solution Approach 2:
The system enables self-service by allowing users to directly query data sources using natural language or simple interfaces without requiring manual data import, transformation, or validation steps. The patent implements automated query generation and execution that handles the complex intermediate steps automatically, letting users focus only on defining their analysis requirements.
2Manufacturing precision
If manual data processing stages are implemented, then data can be properly analyzed, but the process requires weeks or months before visibility is gained into the data
Solution Approach 1:
The system replaces the mechanical manual process of data discovery, import, and validation with an automated computational system. The patent implements automated metadata extraction, query generation, and execution that performs all necessary processing steps through software automation rather than manual intervention, maintaining accuracy while dramatically increasing speed.
Solution Approach 2:
The system changes the parameter of processing time from weeks/months to minutes/seconds by implementing automated parallel processing of metadata extraction, query generation, and execution. The patent transforms the time parameter through technological advancement in automated data processing systems that can handle multiple data sources simultaneously.
3Adaptability or versatility
If domain experts attempt to interact with technical data systems, then they can access data, but they lack the technical expertise to effectively query and analyze the data
Solution Approach 1:
The system acts as an intermediary between domain experts and technical data systems by automatically translating high-level user requirements into technical queries. The patent implements an automated query generation mechanism that converts natural language or conceptual data requirements into executable queries against heterogeneous data sources, eliminating the need for users to learn technical querying languages or understand system complexities.
4Ease of operation
If technical users interact with data sources, then they can access data, but they lack domain knowledge to identify the correct information needed
Solution Approach 1:
The system implements feedback by allowing domain experts to review and refine the automatically generated queries and results. The patent includes mechanisms where users can provide feedback on the relevance of discovered data and generated queries, which the system uses to improve subsequent query generation and data selection, ensuring domain knowledge is incorporated while maintaining technical automation.
Data Source
AI summary
An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.