Skeleton-Query Validation for Automated Data Dictionaries

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Solution Overview

Problem

The manual generation and validation of data dictionaries is time-consuming and prone to errors, especially in data science projects involving large datasets, leading to inconsistencies and inefficiencies.

Innovation Solution

Automated generation and validation of data dictionaries using database queries to identify reference term tokens, generate data dictionaries, and validate them against a skeleton query to ensure accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual generation and validation of data dictionaries is performed, then data documentation can be created, but the process is time-consuming and prone to errors

Engineering Contradiction:

Improveaccuracy of data dictionaryVSAvoidtime for data dictionary generation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of creating and validating data dictionaries with an automated computer-based system. The system uses database queries to automatically extract table and column information, generates data dictionaries programmatically, and validates them against the database schema, eliminating manual entry and review processes that were time-consuming and error-prone.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables the data dictionary generation process to be self-service by automatically querying the database for required information and generating the data dictionary without human intervention. The validation process also occurs automatically by comparing the generated data dictionary against the actual database schema, allowing the system to self-correct and ensure accuracy without manual verification.

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If manual entry of data dictionary information is performed, then data documentation can be created, but human error is extremely prone

Engineering Contradiction:

Improveease of data dictionary creationVSAvoidaccuracy of data dictionary

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent replaces manual entry processes with automated database querying and programmatic generation. The system directly extracts table names, column names, and metadata from the database using structured queries, eliminating the need for manual typing and copying that led to errors. The automated validation further ensures reliability by programmatically checking the generated data dictionary against the actual database schema.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated generation using database queries is implemented, then efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvespeed of data dictionary generationVSAvoidcomplexity of generation system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the data dictionary generation process into distinct automated components: database query execution, result parsing, data dictionary assembly, and validation. Each component handles a specific task, making the overall complex process manageable and maintainable. The system queries the database for schema information, parses the results to extract relevant metadata, assembles the data dictionary in a structured format, and validates it against the database schema.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12436925B2Systems and methods for automated data dictionary generation and validation

Publication Date: 2025.10.07 WELLS FARGO BANK NA

AI summary

Systems, apparatuses, methods, and computer program products are disclosed for automated generation and validation of a data dictionary. An example method includes automatically generating a data dictionary based on one or more reference term tokens of a database query. The example method further includes validating the data dictionary by generating, based on the database query and the data dictionary, a skeleton query, and determining, based on the skeleton query, a validation status of the data dictionary.