Machine Learning for Legacy Business Logic and Data Lineage
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Solution Overview
Problem
Legacy systems lack an efficient method to define business logic for migrating from historical data to modern solutions, due to a lack of documentation and complex code layers, and limited subject matter expert knowledge, making it challenging to reverse engineer business logic and data lineage.
Innovation Solution
A system utilizing machine learning algorithms to identify data patterns, generate pseudo code, and transform data from legacy systems to target systems by extracting datasets, correlating features, and creating a target mapping model to automatically generate pseudo code, enabling efficient business logic definition and data lineage analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reverse engineering of business logic is performed by subject matter experts, then business logic can be defined with high accuracy, but the time and effort required increases significantly
Solution Approach 1:
The patent introduces machine learning models as an intermediary between legacy code and business logic definition. The ML models analyze historical code and data to automatically generate business logic definitions, acting as a mediator that reduces the manual effort required while maintaining accuracy through iterative refinement and expert validation.
Solution Approach 2:
The system creates copies of business logic patterns from historical code and existing data relationships. By learning from past implementations and replicating successful patterns, the system can quickly generate business logic definitions without manually analyzing each case from scratch, significantly reducing time while maintaining consistency and accuracy.
2Loss of information
If deep analysis of legacy code layers is performed to trace data lineage, then complete lineage information can be obtained, but the complexity and time required increases dramatically
Solution Approach 1:
The patent extracts and focuses on critical lineage information rather than analyzing every layer of legacy code. The machine learning model identifies and extracts only the most relevant data flow paths and transformations, filtering out unnecessary complexity while maintaining complete lineage tracking for essential business attributes.
Solution Approach 2:
The system changes the approach from static code analysis to dynamic data-driven analysis. By using machine learning models trained on historical data execution patterns, the system can trace lineage through actual data flows rather than statically analyzing all code layers, reducing complexity while maintaining completeness.
3Measurement precision
If extensive manual effort is dedicated to defining business logic for complex attributes, then accurate business logic can be achieved, but productivity remains low
Solution Approach 1:
The system enables self-service business logic definition through machine learning models that automatically generate definitions based on historical data and code patterns. Business users can interact with the system to define their own logic requirements, and the ML models handle the complex analysis and generation tasks, dramatically increasing productivity while maintaining accuracy through validation mechanisms.
Solution Approach 2:
The patent performs preliminary analysis and pattern recognition before actual business logic definition is needed. The machine learning models pre-process historical code and data relationships to create reusable templates and patterns, so when business logic needs to be defined, the system can quickly generate accurate definitions by adapting these pre-prepared artifacts rather than starting from scratch.
Data Source
AI summary
An embodiment of the present invention is directed to implementing machine learning to define business logic and lineage. The system analyzes data patterns of SORs as well as consumption attributes to define the business logic. An embodiment of the present invention may achieve over 95% match rate for complex attributes. When provided with thousands of SOR attributes, the innovative system may identify a handful of relevant SOR attributes required as well as the business logic to derive the consumption attribute.