Data Source Relationship Visualization for Data Warehouse Migration
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Solution Overview
Problem
Current technologies fail to effectively visualize the relationships between multiple data sources when migrating data to a target data warehouse or virtual data warehouse, making it difficult to determine dependencies and link depths in data transformations.
Innovation Solution
A computer-readable storage medium with executable instructions that analyzes data source tables to determine key relationships and generates graphical representations of these relationships, ordering them based on the depth of links between data source tables to facilitate clear visualization of connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data sources are displayed in traditional GUI displays, then data flow and transformation can be visualized, but the relationships between data sources cannot be clearly illustrated
Solution Approach 1:
The patent transitions from traditional linear or hierarchical data source displays to a two-dimensional graphical representation where data sources are positioned based on their relationship depth. This dimensional change allows simultaneous visualization of multiple data sources and their interrelationships, making previously hidden connection information visible and accessible.
Solution Approach 2:
The patent introduces a graphical representation as an intermediary layer between the data sources and the user. This intermediary visualizes the relationships between data sources, acting as a mediator that translates complex dependency information into an intuitive graphical format, thereby revealing relationship information that would otherwise be lost in traditional data migration tools.
2Productivity
If multiple data sources are combined in ETL or EII processes, then data consolidation is achieved, but dependency relationships between data sources become difficult to determine
Solution Approach 1:
The patent employs visual differentiation through color or graphical styling to indicate the depth and nature of relationships between data sources. By visually distinguishing different relationship levels (e.g., direct vs. indirect dependencies), the system makes dependency detection intuitive and accessible, allowing users to quickly identify relationship patterns without manually analyzing complex data flow paths.
3Stability of the object's composition
If tree structure of related data sources is used, then data organization is achieved, but visual indicators of broken links and link depth are difficult to assess
Solution Approach 1:
The patent segments the data source relationships into distinct visual elements, where each data source is represented as a separate graphical node and relationships are depicted as distinct connecting lines. This segmentation allows clear visualization of link depth and broken connections, as each relationship can be individually assessed visually without navigating through nested tree structures.
Data Source
AI summary
A computer readable storage medium includes executable instructions to receive a selection of two or more data source tables from one or more data sources, determine a data source table that defines keys, and receive mapping information that defines one or more column relationships between the data source table and a second data source table. Relationship structures between the data source tables are analyzed. The data source tables and the relationship structures are graphically represented in a sequential order where the order in which the data source tables are depicted is based on a depth of a relationship between the data source table that defines keys and a second data source table.