Data Warehouse Query Interface Using Templates and Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to provide non-technical users with efficient means to interact with complex data warehouses using natural language queries, leading to a knowledge gap between end-users and technical experts, and lack of actionable insights for decision-making.
Innovation Solution
A system and method that utilizes an embedding model to transform natural language questions into vector representations, matches them with standardized question templates, generates query code using a generative AI model, and produces visualizations based on user preferences, bridging the gap between technical complexity and user accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language-based database interaction methods are used, then ease of operation is improved for non-technical users, but reliability deteriorates due to lack of understanding of end-to-end databases and data partitioning
Solution Approach 1:
The patent introduces an intermediary system consisting of a vector database storing standardized question templates and an embedding model. This intermediary layer translates non-technical natural language queries into standardized database queries by matching embedded representations, thereby enabling ease of operation while maintaining reliability through standardized query patterns that understand data partitioning and relationships.
Solution Approach 2:
The system performs preliminary action by pre-storing standardized question templates and their corresponding database queries in a vector database. This preparation allows the system to reliably translate new natural language queries into accurate standardized queries without requiring real-time analysis of complex database schemas, thus ensuring both ease of operation and reliability.
2Measurement precision
If technical teams perform analytics using custom applications and reports, then measurement precision is improved, but loss of time increases due to considerable development time required
Solution Approach 1:
The patent creates a copying mechanism where standardized question templates represent pre-validated analytical queries. Instead of developing custom applications from scratch, the system copies and adapts proven query patterns stored in the vector database, achieving measurement precision while dramatically reducing development time through template-based generation rather than从零 development.
Solution Approach 2:
The system performs preliminary action by pre-developing and storing standardized question templates that encapsulate proven analytical logic. This preliminary creation of optimized queries eliminates the need for time-consuming development cycles while maintaining measurement precision, as the templates are pre-validated and can be quickly instantiated for new queries.
3Ease of operation
If end-users rely on IT and applications teams to translate queries, then ease of operation is improved for non-technical users, but loss of time increases due to translation and analysis requirements
Solution Approach 1:
The patent enables self-service by allowing end-users to directly execute analytics queries through natural language interaction with the vector database and embedding model. The system autonomously translates queries, executes them against the data warehouse, and generates visualizations without requiring IT or applications teams for translation, thus maintaining ease of operation while eliminating the time loss associated with manual translation and analysis.
Solution Approach 2:
The intermediary system consisting of the embedding model and vector database enables direct self-service by automatically handling the translation between natural language and database queries. This intermediary layer eliminates the need for human translation services from IT teams, allowing end-users to perform analytics independently while reducing time loss through automated processing.
4Adaptability or versatility
If domain experts understand application relationships, then adaptability is improved for business contexts, but device complexity increases when accessing underlying database tables with non-intuitive field names
Solution Approach 1:
The patent introduces an intermediary layer through the vector database that stores standardized question templates mapping business concepts to database table structures. This intermediary translates domain expert knowledge of application relationships into standardized queries that understand underlying database field names, thereby maintaining adaptability to business contexts while reducing device complexity by abstracting away the technical database layer.
Solution Approach 2:
The system performs preliminary action by pre-establishing mappings between business domain concepts and database table structures in the vector database. This preliminary mapping allows domain experts to query using familiar business terminology while the system automatically translates to the appropriate database structure, maintaining adaptability without exposing the complexity of underlying field names.
Data Source
AI summary
Systems and methods may utilize a system configurator to populate an information template associated with a data warehouse (DWH) to generate standardized context information. A DWH query code generator, may use an embedding model to obtain an embedded user question, perform a first similarity matching process to match the embedded user question with a standard question template, extract parameters from the user question to populate the standard question template, populate the standard question template with the extracted parameters to generate a final question, and provide the final question and the standardized context information to a generative AI model that converts the final question into a query code. A visualization code generator may then obtain data related to the user question, perform a second similarity matching process to match the embedded user question with a visualization script, and apply the visualization script to the data to generate a visualization.