Database Domain Extensions for Cross-Domain Data Integration
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Solution Overview
Problem
The integration of diverse data platforms across different domains in the energy industry is challenging due to differing data types, workflows, and legacy applications, leading to siloed data systems that hinder data-driven regulation and end-to-end business optimization.
Innovation Solution
A computing system with an extensible framework that includes a data ingestion module, domain extensibility module, and machine learning model module, enabling the integration and storage of data from various domains, along with connectivity to legacy and cloud applications through file system mounting and network accessible endpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate data platforms are built for each domain (exploration, drilling, production), then each domain can have customized data handling optimized for its specific requirements, but integration between domains becomes challenging and creates siloed systems
Solution Approach 1:
The system segments data platforms into domain-specific extensions (exploration, drilling, production) that can be independently developed and optimized, while maintaining a common core platform. Each domain extension handles its specific data requirements independently, reducing integration complexity while preserving domain customization capabilities.
Solution Approach 2:
The common core platform provides universal functionality across all domains, including unified data storage, standardized APIs, and shared infrastructure. Domain-specific extensions build upon this universal foundation, allowing each domain to have customized handling while benefiting from shared capabilities that reduce overall system complexity.
2Ease of manufacture
If data platforms are built with hardcoded functionality for specific domains, then development time for each domain is reduced, but adaptability to handle diverse data types across domains is limited
Solution Approach 1:
The system uses dynamic domain extensions that can be configured and adapted to handle different data types within each domain. Rather than hardcoded functionality, the extensions use parameterizable schemas and configurable processing logic that maintain development efficiency while providing flexibility for diverse data requirements.
Solution Approach 2:
Domain extensions use parameterizable data schemas and configurable processing parameters that allow the same extension framework to handle multiple data types. This enables rapid adaptation to different data formats and structures without requiring complete redesign, maintaining both development efficiency and flexibility.
3Quantity of substance
If legacy applications with different file architectures are integrated, then data from multiple sources can be consolidated, but compatibility issues and data loss may occur
Solution Approach 1:
The system introduces standardized intermediate representations and conversion layers that act as intermediaries between legacy applications with different file architectures and the unified data platform. These intermediaries translate and normalize data from various formats, ensuring accurate consolidation while maintaining data integrity through validated transformation processes.
Data Source
AI summary
A computing system that includes one or more server computing devices including one or more processors configured to execute instructions for a domain extensibility module that provides software development tools for building domain extensions for a database platform, and a data ingestion module that provides software development tools for defining a metadata schema for extracting metadata from data files. The one or more processors are configured to receive a set of data from a user computing device, define a target metadata schema that includes one or more metadata fields that will be populated during a data ingestion process, define a target domain extension that defines one or more data types for storing the received set of data after performing the data ingestion process, and ingest the received set of data using a metadata extraction pipeline to generate metadata files based on the target metadata schema.