Cross Reference Data Segmentation for Lookup Performance
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Solution Overview
Problem
Large cross-reference database tables experience performance bottlenecks due to increased lookup times as they grow in size, making efficient data sharing and integration between disparate software applications difficult.
Innovation Solution
The method involves augmenting definition files with multi-table data, creating and populating multiple tables based on these definitions, transposing and migrating data from a single cross-reference table to these multi-tables, and validating the migration process to improve lookup performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross reference data is stored in a single database table, then data integration between applications is enabled, but lookup time increases and performance bottlenecks occur as the table grows
Solution Approach 1:
The patent divides the single large cross-reference table into multiple smaller tables segmented by application pairs. Each table contains cross-reference data for a specific source application and target application combination, reducing the number of rows per table and improving lookup performance while maintaining data integration capabilities across all applications.
2Adaptability or versatility
If a single large cross-reference table is used, then comprehensive data mapping is achieved, but system performance deteriorates due to increased table size
Solution Approach 1:
The cross-reference data is segmented into multiple application-specific tables, each handling a subset of the total data mapping requirements. This segmentation reduces the processing load per table while collectively maintaining comprehensive data mapping coverage across all source and target applications.
Solution Approach 2:
The patent introduces an additional dimension to the data organization by creating tables indexed not just by cross-reference identifiers but also by application pair combinations. This multi-dimensional organization allows for faster queries by directly accessing the relevant application-specific table rather than searching through a single large table.
3Ease of operation
If cross reference data is stored in one table, then data sharing between applications is simplified, but error potential increases due to manual reentry requirements
Solution Approach 1:
By segmenting the cross-reference data into multiple tables organized by application pairs, the system enables automated data transformation between applications using the segmented tables as reference. This reduces manual reentry errors while maintaining simplified data sharing through the structured table organization.
Data Source
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AI summary
Embodiments of the present invention can be used to improve cross reference look-up performance by performing multi-table data segmentation. In accordance with an embodiment, a method of multi-table data segmentation can comprise augmenting each of a plurality of definition files associated with a database table with multi-table data. The method can further comprise creating a plurality of multi-tables. Each multi-table is associated with a different one of the plurality of definition files. The method can further comprise transposing data stored in the database table based on the plurality of definition files, and migrating the transposed data from the database table to the plurality of multi-tables.