ML Framework for Database Migration Effort Estimation

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Solution Overview

Problem

Legacy computing and database systems face challenges in transitioning to new systems due to a lack of readily available information on the effort required for migration, leading to inefficiencies and increased manual analysis and planning.

Innovation Solution

A machine-learning-based framework is developed to analyze current systems and provide estimates and recommendations for transitioning to target systems, using trained machine-learning algorithms to classify changes and estimate implementation times, thereby reducing manual effort and improving transition efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis and planning is used to assess transition effort, then detailed understanding of migration requirements can be achieved, but time consumption and resource usage increase significantly

Engineering Contradiction:

Improvetransition effort assessment accuracyVSAvoidtransition planning time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis with automated machine learning algorithms. The system uses trained ML models to analyze codebases, database schemas, and system configurations, automatically generating transition effort estimates without requiring extensive manual inspection while maintaining assessment accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary automated analysis system that acts as a bridge between the current legacy system and the target system. This intermediary system uses machine learning to assess transition requirements, providing detailed migration insights without requiring direct extensive manual analysis of both systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If comprehensive system analysis is performed to identify all necessary changes, then transition completeness is improved, but complexity of the analysis process increases

Engineering Contradiction:

Improvetransition completenessVSAvoidanalysis process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the comprehensive system analysis into multiple specialized machine learning models, each handling specific aspects such as code compatibility analysis, database schema migration assessment, and configuration file evaluation. This segmentation maintains transition completeness while reducing the complexity of any single analysis component.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal automated analysis platform that handles multiple types of system components (code, databases, configurations) through integrated machine learning models. This universal system provides comprehensive transition analysis without requiring separate complex analysis processes for each component type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If detailed change identification is performed in custom code, then accuracy of transition planning is improved, but manual effort and cost increase

Engineering Contradiction:

Improvechange identification accuracyVSAvoidtransition planning efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual code inspection with automated machine learning-based code analysis. The system uses trained models to scan and analyze custom codebases, identifying required changes with high accuracy while eliminating the need for extensive manual review, thereby improving planning efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables the system to perform self-analysis of its own codebase and configuration files using integrated machine learning capabilities. The automated system identifies changes in custom code without requiring external manual intervention, maintaining high identification accuracy while improving productivity.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11651272B2Machine-learning-facilitated conversion of database systems

Publication Date: 2023.05.16 SAP SE

AI summary

An improved system and process for machine-learning upgrade analysis and training thereof is provided herein. A request to analyze the time to upgrade a current system to a target system may be received. A change list having one or more changes for the target system may be read. Custom code for the current system may be compared to the change list to identify recommended changes to the custom code to upgrade the custom code to be compatible with the target system. The recommended changes may be classified into one or categories respectively via a trained first machine-learning algorithm. Time to upgrade the custom code for the respective classified changes may be estimated via a trained second machine-learning algorithm. The recommended changes, the classifications of the recommended changes, and the time estimates of the recommended changes may be provided.