Machine Learning Data Pruner for Database Storage Optimization
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Solution Overview
Problem
Large entities face inefficiencies and increased costs due to inconsistent data pruning strategies across various data storage systems, leading to unnecessary storage of unused information and increased costs for data storage.
Innovation Solution
A data pruner platform utilizing machine learning to process primary and secondary database information, generating suggested pruning parameters to remove unnecessary data from primary databases and transfer it to secondary databases, thereby optimizing storage and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data pruning strategies are implemented manually or with simple rules, then ease of operation is improved, but storage costs increase due to inconsistent and inefficient pruning
Solution Approach 1:
The system implements self-service by using machine learning models to automatically analyze access patterns and generate optimal pruning parameters without requiring manual intervention. The ML model processes database information, identifies unused data, and determines pruning strategies autonomously, freeing users from complex manual pruning operations while optimizing storage costs.
Solution Approach 2:
The invention changes parameters by transforming manual pruning rules into automated ML-based parameter generation. The system adjusts pruning parameters dynamically based on analyzed access patterns, transitioning from static manual configuration to adaptive automated parameter optimization that balances ease of operation with cost efficiency.
2Loss of energy
If machine learning models are used to automatically determine pruning parameters, then storage costs are reduced through efficient data pruning, but device complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary component that bridges the gap between raw database information and optimal pruning decisions. It processes database schemas, access patterns, and query logs to generate pruning parameters, acting as a mediator that simplifies the overall system architecture while enabling intelligent automated pruning.
Solution Approach 2:
The invention replaces mechanical manual pruning operations with automated machine learning-based systems. Instead of requiring users to manually analyze and configure pruning strategies, the ML model substitutes human expertise with automated algorithms that process data patterns and generate optimal pruning parameters, reducing operational complexity despite introducing computational complexity.
3Device complexity
If manual data pruning is performed, then device complexity remains low, but productivity decreases due to time-consuming analysis and inconsistent results
Solution Approach 1:
The system ensures continuity of useful action by continuously monitoring access patterns and automatically adjusting pruning parameters. The machine learning model operates continuously to analyze database usage patterns, generating and updating pruning strategies without interruption, thereby maintaining high productivity while managing complexity through automated continuous optimization.
Solution Approach 2:
The invention implements feedback mechanisms where the machine learning model continuously analyzes access patterns, query logs, and database usage metrics to refine pruning parameters. This feedback loop enables the system to learn from actual data access behavior and improve pruning efficiency over time, significantly enhancing productivity while keeping the system manageable through automated adaptation.
Data Source
AI summary
A device receives, from a user device, a request to prune a primary database, and receives primary database information associated with the primary database and secondary database information associated with a secondary database that is different than the primary database. The device processes the primary database information and the secondary database information, with a machine learning model, to generate suggested pruning parameters, and provides the suggested pruning parameters to the user device. The device receives selected pruning parameters from the user device, where the selected pruning parameters are selected from the suggested pruning parameters or are input via the user device. The device removes pruned information from the primary database based on the selected pruning parameters, and provides the pruned information to the secondary database based on the selected pruning parameters.