Database Validation Rules With ML Error Prediction for Transactions
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Solution Overview
Problem
Existing database management systems face challenges in predicting and mitigating latent errors that arise from transactions, which may not cause immediate issues but can lead to future errors or unexpected results.
Innovation Solution
Implementing database validation rules with business logic and using a trained machine learning model to predict errors based on failed validation rules, and suggesting solutions to remediate these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database validation rules are implemented to check transactions, then data reliability is improved, but system complexity increases
Solution Approach 1:
The system performs validation rules before transactions are committed to the database. By executing validation logic in advance (prior to the transaction completing), the system ensures data integrity is maintained without requiring complex post-transaction checking mechanisms. This preliminary validation approach simplifies the overall system architecture while improving reliability.
Solution Approach 2:
The patent introduces a validation rule engine as an intermediary layer between the transaction processing system and the database. This mediator component handles the complexity of validation logic separately, allowing the main system to remain simpler while still achieving high data reliability through the intermediary's specialized validation functions.
2Reliability
If machine learning models are used to predict errors, then error detection capability is improved, but computational resources increase
Solution Approach 1:
The system applies machine learning models selectively rather than universally. Instead of analyzing every possible transaction with full computational power, the ML models predict only the most likely error scenarios based on historical data patterns. This partial application of computational resources to high-probability error cases improves error detection capability while minimizing unnecessary computational expenditure.
Solution Approach 2:
The patent utilizes machine learning models that process and analyze transaction parameters to predict errors. By transforming transaction data into feature vectors and using these parameters for prediction, the system efficiently detects potential errors without requiring exhaustive computational analysis of all possible transaction states, thus optimizing the balance between detection capability and resource consumption.
3Manufacturing precision
If comprehensive validation rules are applied to all transactions, then data accuracy is improved, but transaction processing time increases
Solution Approach 1:
The validation rules are segmented into different categories and priorities. The system applies comprehensive validation to critical transactions and data, while using lighter validation checks for less critical operations. This segmentation allows the system to maintain high data accuracy where needed without uniformly increasing processing time across all transactions.
Solution Approach 2:
The validation approach is dynamic rather than static. The system adjusts the level of validation applied based on real-time conditions, transaction type, data sensitivity, and historical error patterns. This dynamic validation strategy ensures data accuracy is maintained for high-risk transactions while reducing processing time for low-risk operations, optimizing the balance between accuracy and productivity.
Data Source
AI summary
Embodiments predict errors using database validation rules. Validation rules can be defined that include business logic for validating transactions performed on a database with a data model. Transactions can be performed using the database, where the database is in a post-transaction state after performance of the transactions. The database can be validated in the post-transaction state by performing the defined business logic for a subset of validation rules, where at least one validation rule fails to validate. Using a trained machine learning model, one or more errors for one or more future transactions can be predicted, the predicted errors being based on the at least one failed validation rule.