Cloud Data Record Correlation and Migration System
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Solution Overview
Problem
Current cloud-based solutions face challenges in efficiently correlating and migrating data records across different cloud service instances, particularly in managing data sharing and updates between various application instances, including handling subsets of data fields, modifying data on the fly, and preventing endless loops in data correlation processes.
Innovation Solution
Implementing a data record correlation and sharing system that allows for data correlation at the application layer, enabling data transformation, supplementation, and processing across different cloud service instances, with features like inbound and outbound processing, data migration, and loop detection using correlation indices and version tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data records are correlated and shared across multiple cloud service instances, then data accessibility and collaboration are improved, but the risk of endless loops and data integrity issues increases
Solution Approach 1:
The system implements a feedback mechanism through loop detection that monitors data correlation operations across cloud service instances. When a correlation operation is initiated, the system tracks the propagation of data updates and detects when updates would cycle back to the source instance, creating an endless loop. Upon detection, the system automatically blocks the problematic correlation operation, thereby maintaining data integrity while preserving the benefits of data sharing.
Solution Approach 2:
The patent introduces an intermediary component that acts as a mediator between cloud service instances during data correlation operations. This intermediary manages the correlation process, validates data requests, and coordinates updates across instances. By inserting this intermediary layer, the system enables data sharing while preventing direct uncontrolled correlations that could lead to endless loops and integrity issues.
2Productivity
If data transformation and supplementation are performed during correlation, then data utility and quality are improved, but processing complexity and time increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring correlation settings, data transformation rules, and supplementation parameters before actual data correlation operations begin. These preliminary configurations are stored and reused during subsequent correlation operations, reducing the complexity and time required for each individual data transformation and supplementation task while maintaining high data utility.
3Loss of energy
If subset of data fields are selected for correlation, then data transfer efficiency is improved, but the complexity of field selection and management increases
Solution Approach 1:
The patent applies partial action by enabling users to select only the specific subset of data fields that need to be correlated, rather than transferring complete data sets. This selective field correlation reduces data transfer volume and improves efficiency. The system provides tools to easily manage and update field selections, balancing the reduced transfer efficiency with manageable field selection complexity.
Data Source
AI summary
A directional correlation entry in a correlation index for a data record shared between a source instance and a target instance is identified. It is determined that a capture event associated with an update to the data record meets a requirement. A sharing version of the data record is created. The sharing version of the data record is provided to the target instance.