Model Management Service for Unified Project History
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Solution Overview
Problem
In organizations with rapid turnover, valuable information about projects and their evolution is often lost due to a lack of integration and understanding among modeling experts, leading to incomplete or fragmented project histories.
Innovation Solution
An integrated model management service that collects metadata from various sources, transforms it into a common context, and enables federated event flows, allowing disparate sources to merge modeling data and provide a comprehensive view, supporting model creation, enrichment, transformation, and history tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If modeling experts work independently on projects, then they can focus on specific modeling tasks, but valuable information about projects and their evolution is lost due to rapid turnover and lack of integration
Solution Approach 1:
The patent merges disparate modeling sources and domains into a unified model management service that collects metadata from multiple sources (code repositories, asset catalogs, documentation systems) and transforms them into a common context. This integration ensures that project history and evolution information are preserved across organizational changes while maintaining the productivity benefits of specialized modeling work.
Solution Approach 2:
The model management service acts as an intermediary between independent modeling experts and the organization's knowledge base. It collects metadata from various sources, transforms it into standardized formats, and stores it in a centralized repository, thereby mediating between the need for specialized independent work and the need for organized knowledge preservation.
2Adaptability or versatility
If multiple disparate sources provide modeling metadata, then comprehensive coverage is achieved, but integration and unified view of the data becomes complex
Solution Approach 1:
The model management service implements universality by accepting metadata from multiple disparate sources (code repositories, asset catalogs, documentation systems) and transforming all of them into a common standardized context. This multi-functional approach allows the system to handle various data formats and sources while maintaining a unified view, thereby achieving comprehensive coverage without proportionally increasing integration complexity.
Solution Approach 2:
The system manages complexity by transforming metadata from different sources into a standardized parameter set and format. By changing the representation parameters of the data during transformation, the system can accommodate diverse input sources while presenting a consistent unified view, effectively managing the complexity through standardization rather than dealing with each source's unique structure.
3Loss of information
If model management service transforms metadata to common context, then integrated view is achieved, but transformation process adds processing time and complexity
Solution Approach 1:
The system performs preliminary transformation actions by pre-defining the common context schema and transformation rules before data ingestion. Metadata is transformed into the standardized format as part of the collection process rather than as a separate post-processing step, thereby reducing the time required for integration while ensuring complete information preservation.
Data Source
AI summary
Model creation and management using a model management service can include obtaining model management data that comprises modeling data that defines a project to model, the project including a network flow, a network system, a device, or a process; detecting, in the model management data, a selection of a template to apply to the project to generate the model; and creating, based on the model management data and the template, the model. The model can include a directory, a data structure, and a diagram that describes the project. The model can be converted into a device-agnostic format version of the model and loaded to a data storage resource as model data that can include the device-agnostic format version of the model.