Executable Graph Models for In-Situ Overlay Ontology Mapping
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Solution Overview
Problem
Conventional ontology mapping using third-party mapper applications prohibits real-time data mapping, leads to data duplication and inefficient storage, compromises data security, and involves significant latency, especially in time-critical domains like healthcare.
Innovation Solution
In-situ ontology mapping within an overlay system using primary and auxiliary executable graph-based models, eliminating the need for third-party mapper applications, enabling real-time data mapping, reducing latency, and enhancing data security by keeping data and processing logic separate and secure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If third-party mapper applications are used for ontology mapping, then data interoperability between different database schemas is achieved, but real-time mapping is prohibited and significant latency occurs
Solution Approach 1:
The patent extracts the mapping logic from external third-party mapper applications and embeds it directly into the overlay system's executable graph-based models. This allows the mapping functionality to be integrated within the same system that processes the data, eliminating the need to export data to external mappers and import results back, thereby enabling real-time mapping without significant latency.
Solution Approach 2:
The overlay system acts as an intermediary layer between different database schemas. The executable graph-based models with overlay nodes serve as mediators that perform ontology mapping internally, allowing data from different schemas to be reconciled and integrated without requiring external mapping applications, thus achieving both interoperability and real-time processing.
2Reliability
If third-party mapper applications are used for ontology mapping, then data interoperability is achieved, but data security is compromised due to data exposure
Solution Approach 1:
The patent extracts the data processing functionality from external third-party systems and brings it into the overlay system itself. By performing ontology mapping within the secure boundaries of the executable graph-based models, sensitive data never leaves the controlled environment, eliminating the security risks associated with data exposure to external mapper applications.
Solution Approach 2:
The overlay system performs ontology mapping autonomously using its own executable graph-based models and overlay nodes. The system serves itself by internally reconciling different database schemas without requiring external assistance, thereby keeping data within the secure system boundaries and eliminating security vulnerabilities associated with third-party data access.
3Reliability
If third-party mapper applications are used for ontology mapping, then data interoperability is achieved, but data duplication occurs leading to inefficient storage
Solution Approach 1:
The patent extracts only the essential mapping logic and metadata from the data itself, separating the transformation rules from the actual data content. The executable graph-based models contain the mapping relationships, while the original data remains in its source format, eliminating the need to create and store duplicate transformed copies of the data.
Solution Approach 2:
Instead of copying and storing complete transformed datasets, the patent uses lightweight overlay nodes that reference and transform data on-demand. The overlay nodes contain references to the mapping relationships rather than full data copies, enabling efficient storage while maintaining the capability to generate transformed views when needed.
4Loss of time
If in-situ ontology mapping is implemented within the overlay system, then real-time data mapping and data security are improved, but system complexity increases due to executable graph-based models
Solution Approach 1:
The patent segments the overlay system into distinct executable graph-based models, each handling specific mapping relationships between database schemas. Overlay nodes are further segmented into individual units that can be independently configured and executed. This modular segmentation makes the complex mapping logic more manageable, maintainable, and easier to update without affecting the entire system.
Solution Approach 2:
The executable graph-based models employ dynamic overlay nodes that can be configured, added, or removed based on specific mapping requirements. The system adapts to different ontology mapping scenarios by dynamically loading only the necessary overlay nodes and graph models, rather than maintaining a static complex structure for all possible mappings. This dynamic approach reduces the operational complexity despite the advanced architecture.
Data Source
AI summary
An overlay system provided, includes processing circuitry and storage circuitry that stores primary executable graph-based models and auxiliary executable graph-based models. Each primary executable graph-based model is mapped to one or more auxiliary executable graph-based models based on various rules. The processing circuitry receives a stimulus associated with the overlay system and identifies, based on the stimulus, a primary executable graph-based model and one or more rules. Further, one or more values associated with an auxiliary executable graph-based model that is mapped to the primary executable graph-based model are retrieved based on the one or more rules. One or more values associated with the primary executable graph-based model are populated based on the retrieved one or more values. The processing circuitry further executes an operation associated with the stimulus based on the primary executable graph-based model that is populated with the one or more values.