Automated Sub-Model Generation for Scalable Data Management
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Solution Overview
Problem
Current techniques for identifying and managing sub-models within domain-driven applications are prone to errors, are time-consuming, and do not naturally lend themselves to reuse, especially when dealing with complex, dynamic data and multiple dimensions, making them impractical for large datasets and scalable applications.
Innovation Solution
The system facilitates the retrieval, loading, and modification of models by obtaining input that identifies objects, attributes, and filters within an environment, generating output that includes entities, interfaces, and operating parameters, and allows for the creation of custom sub-models through an intuitive interface, enabling efficient data extraction and management across multiple dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional techniques (flow diagrams, manual analysis, user recollections) are used to identify sub-models, then implementation is possible for simple cases, but the process becomes error-prone, time-consuming, and impractical for large datasets and complex applications
Solution Approach 1:
The system enables self-service by automatically generating sub-models from instance data without requiring manual analysis or user recollection. The automated generation process eliminates human error and significantly reduces the time required, especially for large datasets where manual techniques become impractical.
Solution Approach 2:
The patent replaces manual mechanical techniques (flow diagrams, human analysis) with an automated computational system. This substitution uses software-based model generation that can process large datasets efficiently, eliminating the time-consuming and error-prone nature of manual identification methods.
2Adaptability or versatility
If manual techniques are used for sub-model identification, then simplicity is maintained for small cases, but the approach does not naturally lend itself to reuse and scalability
Solution Approach 1:
The system achieves universality by creating a reusable framework where generated sub-models can be applied across multiple domains and use cases. The automated generation process produces standardized sub-models that can be easily reused and adapted, eliminating the need to recreate models for similar applications.
Solution Approach 2:
The patent applies segmentation by breaking down complex models into reusable sub-models that can be independently managed and combined. This segmentation enables better organization, reuse, and adaptation of model components across different applications while maintaining manageable complexity through systematic structure.
3Productivity
If traditional approaches are used to manage data relationships, then basic functionality is achieved, but operational overhead increases and cost efficiency decreases for large-scale applications
Solution Approach 1:
The system performs preliminary action by pre-generating and organizing sub-models before they are needed for actual data management tasks. This preparation work is done automatically and efficiently, reducing the operational overhead during actual use and improving overall productivity for large-scale data management.
Solution Approach 2:
The patent uses copying by generating reusable sub-models that can be replicated and applied across multiple contexts. This eliminates the need to manually recreate or re-analyze data relationships for each new task, significantly reducing operational overhead and improving efficiency for large-scale applications.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, presenting a first model representative of a communication system, receiving, based on the presenting of the first model, a first input that identifies a first communication device included in the communication system, responsive to the receiving of the first input, presenting values for a plurality of operating parameters associated with the first communication device, receiving a second input that includes a modification of at least one value of a parameter included in the plurality of operating parameters, identifying, based on the receiving of the second input, a second model that is dependent on the first model, and modifying the second model based on the identifying of the second model. Other embodiments are disclosed.