Natural Language Data Transformation System
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Solution Overview
Problem
Users without extensive knowledge of Structured Query Language (SQL) syntax face difficulties in transforming and cleansing data from multiple databases for analysis, as existing methods require writing SQL statements, which can be cumbersome and require extensive SQL knowledge.
Innovation Solution
A system that allows users to request data transformations without using SQL syntax, automatically determining and executing SQL statements to transform datasets, while maintaining data consistency and relationships with underlying databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SQL syntax is used for data transformation, then transformation precision and database control are improved, but operation complexity and user knowledge requirements increase
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates user-friendly natural language queries into precise SQL statements. This mediator layer allows users to interact with the database using simple language while the system automatically generates and executes the corresponding SQL code, thus maintaining transformation precision without requiring users to learn SQL syntax.
Solution Approach 2:
The patent replaces the mechanical requirement of manually writing and debugging SQL syntax with an automated natural language processing system. Instead of users directly manipulating SQL code (mechanical approach), the system uses AI-driven language understanding to automatically generate and execute transformations, substituting the manual mechanical process with an intelligent automated system.
2Measurement precision
If SQL syntax knowledge is required, then data transformation accuracy is improved, but user accessibility and ease of use deteriorate
Solution Approach 1:
The natural language processing intermediary serves as a bridge between users without SQL knowledge and the database system. It translates diverse natural language expressions into accurate SQL queries, allowing users from different backgrounds to access data transformation capabilities while maintaining high accuracy through the system's understanding of data relationships and constraints.
Solution Approach 2:
The system implements a universal interface that accepts natural language inputs from any user regardless of their technical background, while internally handling multiple functions including query generation, validation, and execution. This multi-functional approach makes the system adaptable to various user types while ensuring accurate data transformation through centralized control.
3Ease of operation
If manual SQL writing is required, then transformation control is improved, but time consumption and productivity decrease
Solution Approach 1:
The system performs preliminary actions by pre-compiling and storing understanding of data relationships, table structures, and valid transformation patterns. When a user submits a natural language query, the system quickly matches the intent against pre-analyzed data models and generates optimized SQL statements, avoiding the time-consuming process of manual SQL writing while maintaining precise transformation control.
Solution Approach 2:
The system enables self-service by automatically generating, validating, and executing SQL transformations without requiring user intervention in the coding process. Users simply state their data transformation needs in natural language, and the system autonomously handles the entire process from query generation to execution, significantly reducing time consumption while maintaining control through automated validation rules.
Data Source
AI summary
In one embodiment, a method includes receiving a data-transformation request in relation to a visualization of a first dataset, wherein the data-transformation request expresses at least one transformation of the first dataset without using Structured Query Language (SQL) syntax, wherein the visualization is characterized by a first SQL statement. In addition, the method includes automatically determining based, at least in part, on a mapping of the visualization to the first SQL statement, a second SQL statement operable to return a second dataset. Further, the method includes causing the second SQL statement to be executed on one or more databases. Moreover, the method includes receiving, in response to the causing, the second dataset. The method also includes updating the visualization to reflect the second dataset.