Stream Relationship Diagram for Enterprise Dependency Analysis
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Solution Overview
Problem
In complex enterprise systems, identifying and isolating relevant changes across interconnected streams is challenging due to intricate dependencies, making it difficult to quickly locate and address issues, especially in real-time decision-making scenarios.
Innovation Solution
A method that involves receiving multiple streams, monitoring their sequence and correlation to generate a multi-dimensional stream relationship diagram, dynamically adjusting stream dimensions, and creating an optimized diagram to reflect changes, allowing for quick identification of affected streams and facilitating real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple interconnected streams are monitored in complex enterprise systems, then the completeness of data collection is improved, but the difficulty of identifying and isolating relevant changes increases
Solution Approach 1:
The system segments the complex stream data into multiple dimensions (e.g., time, source, type, priority) and creates separate analysis paths for each dimension. This segmentation allows the system to maintain complete data collection while making it manageable to identify relevant changes by analyzing each dimension independently rather than dealing with the entire complex dataset at once.
Solution Approach 2:
The patent introduces multi-dimensional analysis by organizing stream data along multiple axes such as temporal dimensions, source dimensions, and thematic dimensions. This dimensional transformation converts the complex identification problem into a structured multi-dimensional space where relevant changes can be isolated more easily by filtering along specific dimension axes.
2Device complexity
If traditional stream monitoring methods are used, then system simplicity is maintained, but the speed of real-time decision-making deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-defining dimensions, pre-establishing correlation rules, and pre-organizing stream data into dimensional structures before actual monitoring begins. This preliminary setup enables faster real-time decision-making during operation without requiring complex processing during the actual monitoring phase, thus maintaining system simplicity while improving productivity.
3Quantity of substance
If all stream dimensions are monitored equally, then comprehensive coverage is achieved, but the ability to quickly identify relevant changes deteriorates
Solution Approach 1:
The system applies local quality by assigning different weights, priorities, or analysis depths to different stream dimensions based on their relevance to specific monitoring goals. Instead of treating all dimensions uniformly, the system can focus computational resources on high-priority dimensions while maintaining comprehensive coverage, thereby reducing the time to identify relevant changes without sacrificing complete data collection.
Data Source
AI summary
Synching multiple streams in a complex enterprise product by collecting and analyzing stream dependency data. Collection and analysis of data for large scale and complex enterprise results in a multi-dimensional relationship diagram that highlights the interconnected dependencies of the streams. This allows enterprise software users to more easily determine and select which stream (or streams) will help the user to perform a given task.