Variable Dependency Schema Bridge for Analytics Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional analytics systems lack the ability to automatically align and synchronize variable dependency metadata between business intelligence reports and predictive models, leading to inconsistencies and inefficiencies in data analysis, requiring manual extraction and comparison which is time-consuming and error-prone.
Innovation Solution
A system that automatically extracts and compares variable dependency schemas from business intelligence reports and predictive models, identifying discrepancies and generating modifications to ensure consistency, thereby enabling the generation of complete and accurate analytical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual extraction and comparison of variable dependencies is performed, then alignment between reports and predictive models can be achieved, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service by automatically extracting variable dependencies from both reports and predictive models, comparing them, and generating alignment recommendations without requiring manual intervention. The automated extraction process queries the metadata repositories directly, and the comparison logic identifies discrepancies automatically, eliminating the need for manual extraction and comparison while maintaining high alignment accuracy
Solution Approach 2:
The patent replaces the mechanical manual process of extraction and comparison with an automated computational system. The server executes automated extraction routines that query metadata repositories using standardized schemas, and employs algorithmic comparison logic to identify discrepancies between report variable dependencies and predictive model variable dependencies, substituting human manual operations with automated mechanical processes
2Ease of manufacture
If separate data modeling is used for reports and predictive models, then each model can be developed independently, but inconsistency and incompleteness of business data view occurs
Solution Approach 1:
The system implements feedback by automatically comparing variable dependencies between reports and predictive models, identifying discrepancies, and generating alignment recommendations. This feedback loop ensures that independent model developments are subsequently validated and aligned, maintaining data consistency without restricting the independence of initial model development
Solution Approach 2:
The patent creates equipotentiality by establishing a common metadata repository structure and standardized variable dependency schemas that both reports and predictive models draw from. This standardized framework ensures that both independent models operate from the same data foundation, enabling consistent data views while preserving development independence
3Productivity
If automated extraction and comparison is implemented, then alignment efficiency is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by designing a multi-functional server that performs extraction, comparison, and recommendation generation within a single integrated platform. The automated extraction process uses universal metadata schemas that work across different report types and predictive model types, and the comparison logic is designed to handle various dependency scenarios, reducing the need for separate specialized systems
Solution Approach 2:
The patent introduces an intermediary metadata repository layer that sits between reports and predictive models. This intermediary standardized schema acts as a mediator that simplifies the automated extraction and comparison processes by providing a common interface, reducing the complexity of direct interactions between diverse report and model systems
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for generating a bridge between analytical models. In an embodiment, a server can extract a first variable dependency schema from a first model (e.g., predictive model or business intelligence report) and a second variable schema from a second model (e.g., predictive model or business intelligence report). The first variable dependency schema includes a first definition of a relationship between a first variable and a second variable. The server can compare the first variable dependency schema and the second variable dependency schema. Furthermore, the server can generate a modification to be made in the second variable dependency schema based on the first definition of the relationship between the first and second variable and outputs the modification to be made to the second variable dependency schema.