Data Enrichment Plug-In for Cross-Platform Schema Mapping

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Solution Overview

Problem

Current data management systems are time-consuming, resource-intensive, and prone to errors, especially when deploying structured querying instructions across different databases, and lack automated methods for data enrichment and augmentation that are not platform-dependent.

Innovation Solution

A plug-in system utilizing an information relation charting engine, analytical metric study engine, and derived record construction engine, coupled with machine learning, to automate data enrichment and augmentation by determining field element correspondences, extracting key features, and generating relevant data metrics for any database platform.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional manual methods are used to build data models and map database relationships, then complete understanding and control of data metrics can be achieved, but the process becomes time-consuming and resource-intensive

Engineering Contradiction:

Improvedata model accuracyVSAvoidtime to build data models

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables automated self-service through the plug-in that automatically discovers database schemas, maps field elements, and generates data models without requiring manual intervention from database administrators, thereby reducing time while maintaining reliability through automated validation mechanisms

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes of database mapping and model construction are replaced by an automated information relation charting engine that uses heuristics analysis and machine learning to automatically determine field element correspondences and generate data models, eliminating human labor while preserving accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If external supervision is used to validate data mappings and metrics, then data quality can be ensured, but the process becomes tedious and challenging

Engineering Contradiction:

Improvedata mapping accuracyVSAvoidoperational simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs self-validation through automated consistency checks and validation rules embedded in the plug-in, eliminating the need for external supervision while maintaining data quality through programmatic verification of field element correspondences and data metric integrity

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements automated feedback loops where the information relation charting engine continuously validates mappings against predefined rules and constraints, providing real-time verification without requiring external human supervision, thus ensuring accuracy while simplifying operations

Inventive Principle:

Principle #23Feedback

3Productivity

If structured querying instructions are deployed across different database platforms, then data analysis can be performed, but the instructions may fail due to platform-specific variations

Engineering Contradiction:

Improvedata analysis efficiencyVSAvoidplatform compatibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The plug-in provides universal platform independence by creating platform-agnostic information relation charts that capture the essential structure and semantics of data relationships without being tied to specific database platforms, enabling the same analytical logic to be deployed across different database systems while maintaining adaptability to platform-specific implementations

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If automated data enrichment is implemented across different platforms, then time and resources are reduced, but the system complexity increases

Engineering Contradiction:

Improvedata enrichment speedVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The plug-in acts as an intermediary layer between diverse database platforms and the data enrichment process, providing a unified interface through information relation charts that abstracts away platform-specific complexities while enabling automated enrichment across multiple platforms, thus increasing productivity without proportionally increasing system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12530326B2Data augmentation and enrichment

Publication Date: 2026.01.20 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

Systems and methods for establishing data augmentation and enrichment within a data repository are disclosed, including a plug-in including an information relation charting engine, an analytical metric study engine, a derived record construction engine, and a configurational layer. The information relation charting engine receives raw data metrics from a production environment, determines a correspondence between field elements associated with each of the source and target schema, and correspondingly generates a mapping. The analytical metric study engine performs analytical analysis on the field elements present in the generated mapping and uses machine learning and previous data log to generate recommendations for the database. The derived record construction engine involves a deep learning model to generate relevant and usable data metrics based on the generated recommendations, which is then mapped to the relational database to generate augmented and enriched database.