Model Transition System Combining Outputs During Transition Period
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Solution Overview
Problem
Existing predictive and descriptive modeling systems lack a structured approach for managing model metadata and outputs, leading to inefficiencies in model transitions, inconsistent data retrieval, and increased development and deployment costs.
Innovation Solution
A computer-implemented method and system that stores model metadata for multiple mathematical models, allows for seamless transitions between model sets by combining outputs during a specified transition period, and provides a centralized repository for managing predictive knowledge, enabling real-time score management and consistent data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a structured approach for managing model metadata and outputs is implemented, then model transition efficiency and data retrieval consistency are improved, but system complexity increases
Solution Approach 1:
The system segments model metadata and outputs into distinct stored representations, separating model definitions from execution results. This segmentation enables independent management of model structures and their outputs, improving transition efficiency while maintaining manageable complexity through organized data architecture.
Solution Approach 2:
The system introduces an intermediary layer for managing model transitions and metadata. This intermediary structure coordinates between different model versions and their outputs, enabling smooth transitions without directly increasing the complexity of core model execution logic.
2Reliability
If model outputs are combined during transition period, then data retrieval consistency is improved, but computation time increases
Solution Approach 1:
The system performs preliminary actions by storing model metadata and outputs in advance during the transition period. This allows consistent data retrieval without requiring continuous computation during queries, as pre-computed and stored outputs can be retrieved directly from the database.
Solution Approach 2:
The system creates copies of model outputs during transition periods, storing them in the database for later retrieval. This copying approach ensures data consistency across model transitions without requiring real-time computation, reducing the time needed for data access during and after transitions.
3Ease of manufacture
If centralized repository for managing predictive knowledge is implemented, then development and deployment costs are reduced, but infrastructure requirements increase
Solution Approach 1:
The centralized repository is designed with multi-functionality, serving multiple purposes including model metadata storage, output storage, transition coordination, and data retrieval. This universal approach consolidates multiple functions into a single infrastructure, reducing overall development and deployment costs while avoiding the need for separate specialized systems for each function.
Data Source
AI summary
Model metadata for each of a plurality of mathematical models is stored. Data describing two sets of models is received. Each of the sets identifies one or more models of the plurality of mathematical models. One of the sets comprises a transition-from set and one of the sets comprises a transition-to set. Data describing a launch date for commencing a transition from executing the model(s) in the transition-from set to executing the model(s) in the transition-to set is received. Data describing a transition period of time for the transition is received. The models in the transition-from set and in the transition-to set are executed during the transition period of time. An output of the execution of the model(s) in the transition-from set is combined with an output of the execution of the model(s) in the transition-to set. The output is stored in a database.