Natural-Language Chat Interface for Heterogeneous Data Queries
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Solution Overview
Problem
Conventional data querying techniques require strong technical expertise due to the complexity of heterogeneous data sources, inconsistent naming schemes, and the need to understand various data stores, making it difficult for users to derive information from multiple data sources.
Innovation Solution
An analysis system uses a language model to generate database queries based on natural language questions, allowing users to query heterogeneous data sources through a chat-interface without requiring technical knowledge, by generating and modifying queries using metadata to map actual data assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional querying techniques are used to access heterogeneous data sources, then data can be retrieved from multiple data stores, but users require strong technical expertise including knowledge of database systems, query languages, and data store structures
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user questions into database queries. This mediator handles the complexity of heterogeneous data sources, query language syntax, and schema mapping, allowing users to interact with complex systems through simple natural language without needing technical expertise in database systems or query languages
Solution Approach 2:
The patent replaces the mechanical system of manual query construction requiring knowledge of SQL syntax and data store structures with an automated language model-based system. The language model automatically generates appropriate queries by understanding natural language intent and mapping it to the underlying data schema, eliminating the need for users to manually construct complex queries
2Adaptability or versatility
If data sources use inconsistent or non-intuitive naming schemes, then data can be stored in various formats from different vendors, but users cannot easily derive the type of information stored in columns or select appropriate tables for queries
Solution Approach 1:
The natural language processing intermediary includes semantic understanding capabilities that interpret user intent and map it to appropriate data assets regardless of their naming schemes. The system maintains a semantic layer that understands the meaning of data columns and tables, allowing users to query data based on conceptual meaning rather than relying on intuitive naming conventions
Solution Approach 2:
The patent transforms the parameter of data asset identification from relying on human interpretation of names to using automated semantic analysis. The language model changes the approach from name-based matching to meaning-based matching, where the system understands what data is needed based on the context and intent of the query rather than keyword matching
3Ease of operation
If a language model generates database queries from natural language questions, then users can query data sources intuitively without technical knowledge, but the generated queries may include hallucinated data asset names that do not correspond to actual data assets
Solution Approach 1:
The patent implements a feedback mechanism where the generated language model queries are validated against the actual data schema. When hallucinated data asset names are detected, the system provides feedback to correct the query by mapping to actual existing assets. This feedback loop ensures query accuracy while maintaining the benefits of natural language input
Solution Approach 2:
The patent performs preliminary validation of generated queries against the data schema before execution. The system proactively checks for hallucinated data asset names and corrects them by mapping to actual assets, preventing query failures before they occur. This preliminary action ensures reliability while maintaining ease of use
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.