Preloaded Dependency Data for Secure Heterogeneous Database Deployment

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Solution Overview

Problem

Existing systems struggle to integrate data of different formats or constraints efficiently, leading to inconsistent security, performance, and productivity issues in database deployment, particularly in user account management systems with varying security levels and update frequencies.

Innovation Solution

A system that determines update rates of source data and preloads data with low update rates into a dependency data structure, integrating metadata information to generate a deployment database that maintains security and performance constraints, thereby automating database creation and deployment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data from multiple source databases with different security levels and update frequencies is integrated manually, then security and consistency can be maintained, but database deployment efficiency and productivity deteriorate

Engineering Contradiction:

Improvesecurity consistencyVSAvoiddatabase deployment efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by analyzing source data characteristics (security levels, update frequencies) and preloading low-update-rate data into a dependency data structure before deployment. This advance preparation automates the integration process while maintaining security consistency, eliminating the need for manual review and significantly improving deployment efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes parameters by dynamically adjusting data loading strategies based on update rates and security levels. Low-update-rate data is preloaded into the dependency data structure, while high-update-rate data is loaded fresh during deployment. This parameter-based differentiation automates security-compliant integration without manual intervention.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If data with different update rates is loaded fresh from source databases during each deployment, then data consistency is maintained, but system performance and deployment time deteriorate

Engineering Contradiction:

Improvedata consistencyVSAvoiddeployment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary loading of data with low update rates into the dependency data structure before deployment. This advance preparation reduces deployment time significantly, as this data does not need to be reloaded from source databases during each deployment operation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts data loading strategies based on update rates. Data is categorized into low-update-rate (preloaded into dependency structure) and high-update-rate (loaded fresh during deployment) groups. This dynamic differentiation maintains data consistency for critical data while optimizing deployment time overall.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If heterogeneous data formats from different sources are integrated without standardized metadata, then integration flexibility is maintained, but integration complexity and error propagation increase

Engineering Contradiction:

Improveintegration flexibilityVSAvoidintegration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system creates a universal dependency data structure that can accommodate heterogeneous data formats from different source databases. This standardized structure serves multiple functions: storing data values, tracking security levels, recording update frequencies, and managing relationships between data elements. This universality maintains integration flexibility while reducing complexity through standardization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The dependency data structure acts as an intermediary between heterogeneous source databases and the target deployment database. It standardizes diverse data formats and attributes (security levels, update rates, relationships) into a unified representation, simplifying integration while preserving the ability to handle different data types and sources.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12353366B1Systems and methods for generating integrated dependency data and metadata artifacts for structured data

Publication Date: 2025.07.08 CAPITAL ONE SERVICES LLC

AI summary

Systems and methods for generating deployment databases based on preloaded data from heterogeneous sources are disclosed herein. The system may receive first structured data. The system may extract first data of a first format and second data of a second format. The system may determine that the first data has a first update rate. The system may determine that the second data has a second update rate. The system may preload the first data by retrieving a first identifier and by storing a first preloaded representation of a first parent dataset for the first data. The system may receive a first request for a deployment database. The system, based on the first request, may retrieve the first preloaded representation. The system may generate the deployment database.