Data Archiving Model Pruning Functional Dependencies
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Solution Overview
Problem
Existing database archiving techniques often fail to accurately model functional dependencies, leading to incomplete or corrupt data archiving, as they assume no initial dependencies and may miss crucial relationships, resulting in unrecoverable transactions and database corruption.
Innovation Solution
The method involves determining an initial data model with fully interdependent functional dependencies, pruning unnecessary dependencies based on data samples, and archiving transactions according to a verified data model to ensure complete and recoverable data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If database archiving techniques assume no initial functional dependencies, then the archiving process is simpler and faster, but the archived data becomes incomplete and potentially corrupt
Solution Approach 1:
The system performs preliminary action by establishing an initial data model with all possible functional dependencies before archiving. This initial model includes every attribute as potentially dependent on every other attribute, creating a comprehensive framework that ensures no dependencies are overlooked during the archiving process.
Solution Approach 2:
Instead of starting with no dependencies and adding them (conventional approach), the invention inverts the approach by starting with all possible dependencies and then removing only those that are not actually present. This inversion ensures completeness while maintaining efficiency, as the default state is comprehensive coverage rather than minimal coverage.
2Reliability
If all functional dependencies are included in the data model, then data completeness is ensured, but the data model becomes more complex and processing time increases
Solution Approach 1:
The system performs preliminary verification of functional dependencies by sampling data before finalizing the data model. This preliminary action allows the system to identify and retain only the actual functional dependencies present in the data, filtering out spurious relationships while maintaining comprehensive coverage.
Solution Approach 2:
The system changes the parameter of data model comprehensiveness by transitioning from a static all-or-nothing approach to a dynamic sampling-based approach. By adjusting the sampling parameters and verification thresholds, the system achieves an optimal balance between model complexity and data completeness.
3Loss of time
If functional dependencies are verified against data samples, then processing time is reduced, but the accuracy of dependency detection may be compromised
Solution Approach 1:
The system applies partial action by using data sampling to verify functional dependencies rather than processing the entire dataset. This partial verification approach is sufficient for achieving the required accuracy level while dramatically reducing processing time, as the sample size is carefully selected to represent the overall data distribution.
Solution Approach 2:
The system implements feedback mechanisms where the verified functional dependencies are used to guide subsequent archiving operations. The feedback loop allows the system to adjust sampling strategies and verification criteria based on initial results, improving accuracy over time while maintaining efficient processing speeds.
Data Source
AI summary
Methods, apparatus and articles of manufacture to archive data are disclosed. An example method to archive data disclosed herein comprises determining an initial data model representing functional dependencies among attributes of the data, the initial data model having fully interdependent functional dependencies among all attributes of the data, pruning one or more functional dependencies from the initial data model to determine a verified data model, and archiving a transaction included in the data to memory according to the verified data model.