Order-Invariant Database Table Comparison Using Row Indicia
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Solution Overview
Problem
Existing methods for comparing order-sequenced database tables fail to accurately identify mismatches due to variations in physical or logical order, primary key discrepancies, and data content differences, leading to potential database divergence and inconsistent results.
Innovation Solution
An automated method generates indicia for each row of the database tables without considering their original order, comparing these indicia to identify mismatches, and optionally repairs the target table based on the mismatched indicia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional row-by-row comparison methods are used on order-sequenced database tables, then the comparison process is simple to implement, but mismatches are not accurately identified due to variations in physical or logical order
Solution Approach 1:
The patent applies preliminary action by generating indicia (such as hash values or checksums) for each row before the comparison process. This pre-processing step creates unique identifiers that remain consistent regardless of row order, allowing accurate mismatch detection without complex sorting or ordering operations during the actual comparison.
Solution Approach 2:
The patent introduces indicia as an intermediary element between the raw data rows and the comparison logic. Instead of directly comparing row contents which are affected by ordering variations, the system compares indicia values that serve as order-invariant representations, thereby resolving the contradiction between accuracy and complexity.
2Measurement precision
If indicia generation and comparison is performed for all rows, then mismatch identification accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by first comparing indicia values for all rows to quickly identify potential mismatches. Only when indicia differences are detected does the system perform more intensive processing on specific subsets of rows, rather than uniformly processing all rows at full detail, thus reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The comparison process is segmented into multiple stages: first generating and comparing indicia for all rows (coarse-grained comparison), then performing detailed row-by-row analysis only on rows where indicia mismatches are detected (fine-grained comparison). This segmentation reduces total processing time by avoiding unnecessary detailed analysis of matching rows.
3Reliability
If database tables are compared without considering row order, then data integrity in redundant systems is maintained, but the method becomes inapplicable to order-sequenced files/tables
Solution Approach 1:
The patent achieves universality by creating a comparison method that works across different file types through the indicia abstraction layer. The same indicia generation and comparison logic can be applied to both order-sequenced files (where row position matters) and unordered data structures (where it doesn't), making the solution broadly applicable while maintaining data integrity for redundant system verification.
Data Source
AI summary
An automated method is provided for comparing a first database table and a second database table with each other to produce a table of differences therebetween. The first database table and the second database table include one or more subsets of data that are intended to match each other. The first database table and the second database table each include multiple rows, each row having multiple columns. A first set of indicia is generated comprising indicia for a subset of the columns of each of the multiple rows of a subset of the first database table, and a second set of indicia is generated comprising indicia for a subset of the columns of each of the multiple rows of a subset of the second database table. The second set of indicia are generated without regard to an original order of the columns in the second database. The indicia in the first set of indicia are compared with the indicia in the second set of indicia on a row-by-row basis, wherein the comparing occurs without regard to an original order of the rows in the second database table. Any indicia in the first set of indicia that are not present in the second set of indicia are identified, thereby identifying a first set of mismatched indicia. Likewise, any indicia in the second set of indicia that are not present in the first set of indicia are identified, thereby identifying a second set of mismatched indicia. The table of differences is populated with the identified indicia. The second database table may be repaired by using at least one of the first set of mismatched indicia and the second set of mismatched indicia.