Relational Database Schema Generation from Text Requirements
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Solution Overview
Problem
Current methods for generating a relational database schema are time-consuming and require specialized technical skills, relying on technical teams to analyze requirements and develop entity relationship diagrams, which limits rapid schema generation directly from input requirements.
Innovation Solution
A computer-implemented method that interprets text characters from a requirements specification using a predefined character encoding, identifies proper and common nouns, and generates a relational database schema script, allowing for rapid schema generation without dependence on specific technical competencies, using a database dictionary-based approach and ASCII interpreter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If technical team members manually analyze requirements and develop entity relationship diagrams, then the schema generation accuracy is improved, but the time consumption and complexity increase
Solution Approach 1:
The system performs self-service by automatically generating database schemas from requirements specifications without requiring manual technical analysis. The computer-implemented method parses requirements, identifies entities and attributes, and generates schemas autonomously, eliminating the need for technical team members to manually create entity relationship diagrams while maintaining accuracy through structured processing of the specification text.
Solution Approach 2:
The patent replaces the mechanical process of manual schema generation with an automated computer-based system. Instead of technical team members manually analyzing requirements and drawing entity relationship diagrams, the system uses text processing, pattern matching, and rule-based algorithms to automatically extract entities, attributes, and relationships from requirements specifications and generate corresponding database schemas.
2Manufacturing precision
If technical team members with specialized skills are used, then the schema generation quality is improved, but the ease of operation deteriorates
Solution Approach 1:
The system eliminates the need for specialized technical team members by performing schema generation automatically. Any user can provide requirements specifications in natural language, and the system will process them through text parsing, entity identification, and schema generation without requiring the user to have specialized database design skills or knowledge of entity relationship diagrams.
Solution Approach 2:
The patent introduces an intermediary system that translates natural language requirements into database schemas. This intermediary process includes parsing the requirements text, identifying entities and attributes through pattern matching, determining relationships between entities, and generating standardized schema outputs. This intermediary layer shields users from the complexity of database design while ensuring quality results.
3Reliability
If manual analysis and entity relationship diagrams are used, then the reliability of schema generation is improved, but the device complexity increases
Solution Approach 1:
The patent replaces complex manual processes with a streamlined automated system. Instead of requiring technical team members to manually create and validate entity relationship diagrams, the system uses automated text processing, pattern recognition, and rule-based relationship inference to directly generate schemas from requirements, reducing procedural complexity while maintaining reliability through consistent application of generation rules.
Solution Approach 2:
The system extracts essential information directly from requirements specifications without requiring the intermediate step of creating entity relationship diagrams. By parsing the text to identify entities, attributes, and relationships, and directly translating them into schema definitions, the system removes the unnecessary intermediate layer while preserving the reliability of schema generation through structured extraction and validation.
Data Source
AI summary
Relational database schema generation includes obtaining an input requirements specification for a relational database to be generated, interpreting text characters of the requirements specification as values of a predefined character encoding, parsing the interpreted text characters and identifying complete sentences of the requirements specification, identifying, using an input dictionary that specifies combinations of character values of the predefined character encoding, proper nouns in the identified complete sentences and common nouns in the identified complete sentences, and generating a relational database schema script for the relational database, the generating including indicating the identified proper nouns of the complete sentences as entities of the relational database and indicating the identified common nouns of the complete sentences as attributes of those entities.