Entity Relationship Diagram for Dynamic Query Visualization
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Solution Overview
Problem
In software environments where users create data applications through user-defined database queries, maintaining and optimizing complex query structures becomes unwieldy due to dynamic relationships, making it difficult to identify performance bottlenecks and errors, especially when queries span multiple documents or containers.
Innovation Solution
An entity relationship diagram is constructed to dynamically discover and represent non-explicit relationships by parsing queries, providing visibility and automation in configuring and optimizing query structures, and integrating performance metrics visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually maintain complex query structures spanning multiple documents, then query functionality can be achieved, but the complexity of maintenance and difficulty of tracking relationships increases significantly
Solution Approach 1:
The patent introduces an intermediary system that automatically discovers and maps relationships between queries across multiple documents. This intermediary mechanism parses query definitions, identifies references to other queries, and constructs a relationship graph that visualizes dependencies, thereby mediating between the complex query structures and the user's need to maintain them.
Solution Approach 2:
The patent replaces the manual mechanical process of tracking query relationships with an automated computational system. Instead of users manually documenting and maintaining relationship maps, the system automatically parses query syntax, extracts relationships, and generates visualizations, substituting the manual mechanical tracking process with automated information processing.
2Reliability
If users manually track and update query relationships across documents, then relationship awareness can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically discovers and updates query relationships without requiring user intervention. The relationship graph is generated and maintained automatically through parsing and analysis of query definitions, allowing the system to serve itself in tracking relationships rather than requiring users to manually maintain this information.
Solution Approach 2:
The patent substitutes the manual mechanical process of relationship tracking with automated computational analysis. The system parses query syntax, identifies references, and constructs relationship maps automatically, replacing the time-consuming manual process with efficient automated information processing that maintains high accuracy.
3Loss of information
If static schema visualization methods are used, then documentation can be produced, but the process is time-consuming and prone to errors due to dynamic schema changes
Solution Approach 1:
The patent transforms static schema visualization into a dynamic process that automatically adapts to schema changes. Instead of producing static documentation that becomes outdated, the system continuously parses query definitions and updates the relationship graph in real-time, ensuring the visualization remains current and complete without requiring manual regeneration.
Solution Approach 2:
The patent replaces the manual mechanical process of creating and updating schema documentation with automated computational parsing and graph generation. The system automatically extracts schema information from queries, builds relationship models, and generates visualizations without human intervention, eliminating errors and time consumption associated with manual documentation.
4Loss of information
If detailed query relationship tracking is implemented, then visibility and control improve, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex query relationship information into manageable visual components through the relationship graph. By dividing the overall system view into individual nodes representing queries and edges representing relationships, the system provides detailed visibility while organizing complexity into structured, visually separable elements that are easier to comprehend and manage.
Data Source
AI summary
An interactive entity relationship diagram discovers explicitly defined relationships, and also dynamically discovers and represents non-explicit relationships. This entails calculating metadata and references by parsing the queries. An entity relationship diagram thereby provides novel visibility on the queries being related as the basis for a user interface which integrates several configuration utilities.