Database Domain Extensions for Cross-Domain Data Ingestion
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Solution Overview
Problem
The integration of diverse data platforms across different domains in the energy industry is challenging due to their siloed nature, varying data types, and differing workflows, making it difficult to achieve seamless connectivity and data management.
Innovation Solution
A computing system with an extensible framework that includes a data ingestion module, domain extensibility module, and machine learning model module, providing software development tools to integrate and manage data from various domains, enabling seamless connectivity and data management across legacy and cloud-based applications.
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 and workflows, but integration between domains becomes challenging and complex
Solution Approach 1:
The patent merges multiple domain-specific data platforms into a single unified data platform that can handle diverse data types from exploration, drilling, and production domains. The system uses a common data ingestion pipeline, metadata schema, and storage infrastructure that can accommodate different data formats and domain requirements without requiring separate integrated platforms for each domain.
Solution Approach 2:
The data platform is designed with universal components that can serve multiple domains simultaneously. The metadata schema framework allows the same platform to understand and manage different data types across domains, while the ingestion pipeline can process various data formats. This multi-functional design eliminates the need for domain-specific separate platforms while maintaining customized data handling capabilities.
2Productivity
If data platforms are designed with fixed domain-specific workflows, then data processing efficiency within each domain is optimized, but flexibility to handle diverse data types and workflows is reduced
Solution Approach 1:
The patent implements dynamic configuration capabilities that allow the data platform to adapt its processing workflows based on the incoming data type and domain requirements. The metadata schema can be dynamically defined and modified to match different data structures, and the ingestion pipeline can dynamically adjust processing steps. This dynamic design maintains high processing efficiency for each domain while providing flexibility to handle diverse data types without requiring fixed domain-specific workflows.
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.