Foreign Key Identification Using Statistical Conformance Scores
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Solution Overview
Problem
Conventional methods for identifying foreign key (FK) relationships in large databases are processor-intensive and prone to false positives, leading to inefficiencies and resource wastage due to inadequate documentation and ineffective matching techniques.
Innovation Solution
A computer-implemented method computes a threshold value for primary key-foreign key (PK-FK) pairs, generates a subset based on a predefined acceptance criterion, determines a conformance score using Cumulative Probability Distribution (CPD) values, and compares it to an acceptability index to optimize FK identification, reducing false positives and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional matching techniques are used to identify FK relationships in large databases, then comprehensive coverage of potential FK pairs is achieved, but processor intensity and computational resources increase significantly
Solution Approach 1:
The patent segments the FK identification process into multiple phases: initial candidate generation using conventional techniques, filtering phase using statistical thresholds, and final validation phase. This segmentation reduces processor intensity by eliminating non-conforming candidates early before intensive validation is performed.
Solution Approach 2:
The patent applies preliminary statistical analysis to compute thresholds and identify conforming PK-FK pairs before performing the computationally intensive validation process. This preliminary action filters out non-conforming pairs early, reducing the workload for subsequent processing stages.
2Adaptability or versatility
If conventional matching techniques are used to identify FK relationships, then all potential relationships are considered, but false positives increase
Solution Approach 1:
The patent implements feedback mechanisms where statistical information from the data distributions is used to adjust and refine the identification process. Conformance scores are computed based on statistical deviations, and this feedback is used to filter candidates, improving precision while maintaining adaptability through configurable thresholds.
Solution Approach 2:
The patent changes the parameters used for evaluation from simple matching criteria to statistical conformance scores based on cumulative distribution functions. This parameter transformation enables more precise differentiation between true FK relationships and false positives while maintaining comprehensive coverage through adjustable acceptance thresholds.
3Measurement precision
If statistical analysis with conformance scores is applied to filter PK-FK pairs, then false positives are reduced, but computational complexity increases
Solution Approach 1:
The patent applies partial statistical analysis only to candidate pairs that pass initial filtering, rather than performing complete statistical validation on all possible pairs. This selective application of complex analysis reduces overall computational complexity while maintaining high precision for the final results.
Solution Approach 2:
The patent replaces complex manual validation mechanics with automated statistical computations. The use of cumulative distribution functions and automated scoring systems substitutes for what would otherwise require complex manual analysis, reducing algorithmic complexity while improving precision.
Data Source
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AI summary
Systems and methods for foreign key identification are described. In one embodiment, the method includes computing a threshold value for each of a primary key-foreign key (PK-FK) pair of a super-set of PK-FK, and generating a sub-set of PK-FK pair based on comparison of the threshold value and a predefined threshold value, where the predefined threshold value is indicative of an acceptance criterion. Further, the method includes determining a conformance score for each of the PK-FK pair of the subset of PK-FK pair, wherein the conformance score is based on deviations between Cumulative Probability Distribution (CPD) values of PK and CPD of FK for each PK-FK pair. Further comparing the conformance score of the each PK-FK pair of the sub-set of PK-FK pairs with a predetermined acceptability index, where the predetermined acceptability index is indicative of an acceptance of at least one positive FK from the set of PK-FK pairs.