Multi-Source Relationship Table for Metadata Lineage Analysis
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Solution Overview
Problem
Current systems face challenges in effectively processing metadata across multiple data sources to identify relationships and characterize the impact and lineage of objects within a metadata repository, leading to issues with duplicate metadata and inconsistent representations.
Innovation Solution
A computer-readable storage medium with executable instructions that processes metadata to list flattened single source object relationships in a multiple source relationship table, using relationship rules to equate objects across different data sources and populate a multi-source relationship table, facilitating analysis and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If metadata is integrated into a single metadata repository from multiple data sources, then the quantity of metadata is increased and centralized, but the complexity of processing and analyzing relationships between objects increases
Solution Approach 1:
The patent segments the complex metadata repository into multiple relationship tables organized by data source. Each relationship table contains relationships specific to one data source, allowing independent processing and analysis. This segmentation reduces the complexity of analyzing all relationships simultaneously while maintaining the ability to analyze cross-source relationships by joining the segmented tables.
2Measurement precision
If relationship rules are applied to equate objects across different data sources, then the precision of identifying same-as relationships is improved, but the time required to process and analyze relationships increases
Solution Approach 1:
The patent applies relationship rules to equate objects across data sources in advance, creating a pre-computed mapping of same-as relationships. This preliminary action stores the results of complex equivalence calculations, so that when lineage and impact analysis is performed, the system can quickly reference pre-determined equivalences rather than recalculating them, significantly reducing processing time.
Solution Approach 2:
The patent maintains continuous relationship analysis by implementing efficient query mechanisms that can quickly retrieve and apply relationship information. The system continuously maintains the relationship tables and same-as mappings, allowing for rapid repeated analysis without reprocessing all raw metadata each time, thus reducing the time loss for subsequent analyses.
3Ease of operation
If flattened single source object relationships are listed in a relationship table, then the ease of querying and reporting is improved, but the volume of data to be processed and stored increases
Solution Approach 1:
The patent segments the relationship data into multiple tables, each containing flattened relationships for a specific data source. This segmentation allows the system to process and store relationships in an organized manner, querying only the relevant segments for specific analyses rather than processing the entire dataset, thus managing data volume efficiently while maintaining ease of querying.
Data Source
AI summary
A computer readable storage medium includes executable instructions to list flattened single source object relationships in a first segment of a multiple source relationship table. Same-as multiple source object relationships are calculated. Same-as multiple source object relationships are then populated in a second segment of the multiple source relationship table.