Declarative Data System Generation from Semantic Ontologies
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Solution Overview
Problem
Current systems for creating and managing integrations and APIs are complex, requiring manual workflows and multiple tools, leading to inefficiencies, errors, and increased costs due to the imperative nature of existing technologies, which results in a brittle and unknowable data system landscape that is difficult to manage and maintain.
Innovation Solution
A declarative approach using semantic ontology information structures to generate and manage data systems, allowing a single user to define integration outcomes through pre-defined ontologies, which are then processed into necessary configuration data structures for API servers, database schemas, and integration middleware, ensuring consistent data meaning and supporting industry standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual workflows and multiple tools are used to create and manage integrations and APIs, then data systems can be created and managed with existing technology, but the system complexity increases and management becomes difficult
Solution Approach 1:
The patent merges multiple separate tools and workflows (data modeling tools, integration middleware, API generators, database management systems) into a single unified declarative system. This consolidation eliminates the need for manual coordination between different tools and roles, directly reducing system complexity while maintaining the ability to create and manage integrations and APIs.
Solution Approach 2:
The declarative system provides universal functionality that can generate multiple types of outputs (integrations, APIs, database schemas, data models) from a single unified approach. This multi-functionality replaces the need for separate specialized tools for each task, reducing overall system complexity while maintaining versatility.
2Adaptability or versatility
If manual workflows are used to coordinate across different roles and tools, then integrations can be created, but the time and effort required increases significantly
Solution Approach 1:
The system enables self-service by automatically generating integrations, APIs, and data models from declarative inputs. The automated generation process eliminates the need for manual coordination between Database Administrators, Application Developers, and Integration Developers, significantly reducing the time required to create and manage integrations while maintaining full functionality.
3Adaptability or versatility
If imperative approaches with multiple specialized tools are used, then data systems can be built, but the cost of creation and maintenance increases
Solution Approach 1:
By merging multiple specialized tools into a single unified declarative system, the patent eliminates the need to purchase, license, and maintain separate software products. This consolidation directly reduces creation and maintenance costs while preserving the ability to build complex data systems with integrations and APIs.
4Adaptability or versatility
If separate tools are used for data modeling, integration, and API generation, then each tool can specialize in its function, but synchronization between tools becomes complex and error-prone
Solution Approach 1:
The patent merges separate data modeling, integration, and API generation tools into a single unified system. This eliminates the synchronization problems between separate tools because all operations occur within one system that maintains data consistency automatically. The unified approach preserves specialized functionality while ensuring reliability through centralized management.
5Adaptability or versatility
If manual coordination between Database Administrators, Application Developers, and Integration Developers is used, then integrations can be created, but the process becomes brittle and unknowable
Solution Approach 1:
The declarative system enables self-service by automatically generating all integration artifacts from unified inputs. This eliminates the need for manual coordination between different roles, making the system easier to manage and less brittle. The automated process maintains adaptability while significantly improving ease of operation through reduced human intervention.
Data Source
AI summary
A method of defining and managing data integration, data storage, programmatic data access and data serving is described, the method comprising: retrieving from memory a set of semantic information models; displaying for a user a set of semantic information models; receiving a selection from the user; based on the selection assembling canonical specification schema artifacts, the canonical specification schema artifacts used to define data integration, storage, programmatic access and serving of data; generating canonical specification schema artifacts, used to define data integration, storage, programmatic access and serving of the data; displaying for the user the canonical integration schema artifacts; receiving a selection from the user whereby the canonical schema is mapped to data sources, and sending the appropriate schema artifacts to appropriate Data System endpoints and configuring the endpoints for operation. A system implementing the method is also described.