Number Validation Check Recovery From Historical Data
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Solution Overview
Problem
In computational systems, inaccuracies in data can lead to significant disruptions and costs when validation checks for numbers become unavailable due to loss or corruption, causing valid data to be falsely rejected and requiring costly remedial actions such as closing bank accounts or generating new ones.
Innovation Solution
An apparatus and method that determine a validation check for a set of unique numbers by applying candidate validation functions and weightings to identify the correct validation check, even when the original validation information is unknown, by iteratively testing combinations to find the one that confirms the validity of the numbers above a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If validation checks are performed using conventional processes, then data inaccuracies can be identified, but validation information may become lost or corrupted preventing the checks from being performed
Solution Approach 1:
The system performs preliminary actions by proactively identifying and recovering validation checks before data inaccuracies occur. It systematically tests candidate validation functions against historical data to reconstruct the correct validation logic, ensuring validation capability is restored before it is needed.
Solution Approach 2:
The system introduces an intermediary recovery mechanism that bridges the gap between lost validation information and current data needs. By using historical data as an intermediary, the system can reverse-engineer the original validation function through systematic testing of candidate functions, thereby recovering the lost validation logic.
2Ease of operation
If validation checks cannot be performed due to unavailable validation information, then data usability is maintained, but valid data may be falsely rejected or accounts closed
Solution Approach 1:
The system implements feedback by using historical data to test and validate candidate validation functions. The feedback loop continuously refines the recovered validation check by comparing test results against known valid historical records, ensuring the recovered function accurately reflects the original validation logic.
Solution Approach 2:
The system performs self-service by autonomously recovering its own validation checks without external intervention. It systematically tests candidate functions against its own historical data, identifies the correct validation logic through the confirmation threshold mechanism, and restores validation capability independently.
3Reliability
If new validation checks are generated to replace lost ones, then validation capability is restored, but significant disruption and computational expense occur
Solution Approach 1:
The system applies partial action by testing only a focused set of candidate validation functions rather than exhaustively searching all possible functions. By leveraging domain knowledge to limit the candidate set and using the confirmation threshold to stop early when success is achieved, it recovers validation checks with minimal computational overhead.
Solution Approach 2:
The system uses disposable candidate validation functions that can be quickly tested and discarded. Each candidate function is a low-cost computational object that serves its purpose in the testing process and is then abandoned, allowing rapid iteration through multiple candidates without significant computational investment in any single failed attempt.
Data Source
AI summary
An apparatus, method and computer program product for identifying a validation check for a set of numbers is provided. The apparatus comprises circuitry configured to: obtain first data comprising a set of unique numbers for which a validation check is unknown; determine a plurality of candidate validation functions to apply to the first data; determine a plurality of candidate weightings to apply to the first data; for each of the plurality of candidate validation functions and each of the plurality of candidate weightings: apply the candidate validation function to the first data; and determine, for each number of the set of unique numbers, whether the candidate validation function confirms that the number is valid; and when the number of confirmations for a candidate weighting and a candidate validation function is above a predetermined threshold, identify the candidate validation function and candidate weighting as the validation check for the first data.