Domain-Specific Entity Identifier Framework for Cross-Model Correlation
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Solution Overview
Problem
Existing object models struggle to correlate conceptually similar entities across different domains due to model-dependent representations, leading to tedious data retrieval processes and limitations when data is private or not exposed.
Innovation Solution
A framework with a common set of domain-specific entity identifier types allows object models to correlate model-specific object instances, using an abstract entity identifier class and extensibility mechanisms to support third-party extensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-dependent application programming interfaces are used to retrieve entities using model-dependent data, then entity correlation can be achieved, but the process becomes tedious and complex
Solution Approach 1:
The patent introduces model-agnostic entity identifiers as an intermediary layer between different object models. These identifiers serve as a universal key system that allows entities from different models (e.g., performance analysis tools, debuggers, source code) to be correlated without requiring direct model-dependent API calls. The identifier acts as a mediator that translates between different model representations, simplifying the correlation process while maintaining accuracy.
Solution Approach 2:
The patent creates a universal entity identifier system that can be used across multiple different object models and tools. Instead of requiring separate model-dependent APIs for each tool, a single universal identifier type can represent entities from any model, enabling one identifier to serve multiple functions across different debugging, performance analysis, and source code contexts.
2Ease of operation
If object models expose entity data publicly with access mechanisms, then entity correlation is possible, but data privacy and security are compromised
Solution Approach 1:
The patent extracts only the essential identifying information from entities and encapsulates it in model-agnostic entity identifiers. Instead of exposing complete entity data or requiring public access mechanisms, the system extracts minimal necessary information (sufficient to uniquely identify the entity) and hides the rest. This extraction approach enables correlation while maintaining data privacy, as the identifiers contain only what is needed for matching entities across models.
3Adaptability or versatility
If third parties need to extend entity identifier types, then system adaptability improves, but system complexity increases
Solution Approach 1:
The patent segments the entity identifier system into distinct, independent components: a base identifier class providing common functionality, and extendable subtypes for specific domains. This segmentation allows third parties to create specialized identifier types without modifying or understanding the entire system. Each segment can be developed, tested, and maintained independently, reducing overall system complexity while enabling extensibility.
Data Source
AI summary
Various technologies and techniques are disclosed for providing a framework with a common set of domain-specific entity identifier types that object models representing conceptually similar entities from a domain can use to correlate to model-specific object instances. A particular model-specific object instance of a particular one of the object models can be retrieved using a common set of domain-specific entity identifier types. An instance of a particular domain-specific entity identifier type can be retrieved from a corresponding one of the model-specific object instances. One or more extensibility mechanisms allow third parties to extend the set of domain-specific entity identifier types.