Context Transfer for Data Storage Integrity
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Solution Overview
Problem
In the exploration and production (E&P) sector, managing large data volumes and ensuring data integrity across different environments is challenging, particularly in ensuring that context information is properly transferred and loaded into data stores without duplication or errors, while adhering to best practices and permissions.
Innovation Solution
A method and system for context transfer that involves creating a task request with context information for data items, transmitting this request to a data manager environment for approval, and loading the data into a master data store only after approval, ensuring compliance with best practices and permissions, and facilitating a tightly coupled approval and notification process between users and administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is loaded directly from analysis environment to data store, then data loading speed is improved, but data integrity and duplication control deteriorate
Solution Approach 1:
The patent creates a task request with context information before actually loading the data. This preliminary action includes generating a unique identifier, storing context metadata, and preparing the data load task without immediately executing the data transfer. This allows for pre-validation and approval processes to ensure data integrity before the actual loading occurs, thus maintaining reliability while enabling efficient batch processing that preserves productivity.
2Manufacturing precision
If context information is transferred with every data load request, then data fidelity is improved, but system complexity increases
Solution Approach 1:
The patent extracts only the essential context information needed for data integrity (such as unique identifiers, source metadata, and context descriptors) from the full analysis environment state. This selective extraction transfers only the necessary contextual metadata rather than entire data contexts, maintaining data fidelity while significantly reducing the complexity overhead of context transfer and processing.
Solution Approach 2:
The patent creates a simplified copy of context information in the form of structured task requests with standardized fields. Instead of transferring complex analysis environment states, it generates standardized task request objects that contain only the essential context needed for data loading and tracking. This copying approach maintains data fidelity through accurate context representation while reducing system complexity through standardization.
3Reliability
If approval process is implemented for data loading, then data quality control is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary validation of context information and data load requests before the approval process. By pre-checking data integrity, validating context completeness, and preparing task requests in advance, the system reduces the amount of work needed during the approval stage. This preliminary action ensures that the approval process only needs to review pre-validated information, maintaining data quality control while minimizing additional processing time.
Data Source
AI summary
A method for context transfer for data storage is disclosed. The method includes receiving, from an analysis environment, a request to load a data item for analysis, creating, based on the request, a task request including context information of the data item, transmitting the task request to a data manager environment, receiving, from the data manager environment, an approval of the task request based on the context information, and loading the data item in a master data store in response to the approval.