Data Flow Graph Layout via Subgraph Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex data flow graphs in graph design make it difficult to efficiently analyze relationships among nodes, particularly in identifying and representing clusters, due to their intricate nature and the need for minimizing crossed edges and edge length differences.
Innovation Solution
A method that transforms data flow applications by replacing subgraphs with combination or logical subgraphs, collapsing composite nodes, and estimating rendering time to simplify the complexity of the graph, using a layout strategy engine that balances detail and rendering efficiency based on user preferences and system constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the data flow graph is rendered with high detail to show all nodes and edges, then the completeness of information is improved, but the rendering time and visual complexity increase
Solution Approach 1:
The graph is divided into clusters of nodes that share common properties or connections. Each cluster is rendered as a unified unit rather than individual nodes, allowing the system to maintain information completeness at the cluster level while reducing the number of individual elements rendered, thus decreasing rendering time.
Solution Approach 2:
Multiple nodes that are closely connected or belong to the same functional group are merged into a single visual representation. This combining approach preserves the relational information between nodes while presenting them as a cohesive unit, reducing the total rendering burden while maintaining informational completeness.
2Ease of operation
If the graph layout minimizes crossed edges and equalizes edge lengths, then the visual clarity and ease of analysis are improved, but the layout complexity and computational requirements increase
Solution Approach 1:
The graph is segmented into discrete clusters that are positioned and sized to minimize edge crossings within each cluster. By treating clusters as atomic units rather than individual nodes, the layout algorithm can achieve cleaner visual arrangements with reduced computational complexity.
Solution Approach 2:
Different regions of the graph are assigned different layout strategies based on their local characteristics. Clusters with similar properties are grouped together and rendered with optimized spacing and positioning, while maintaining overall visual clarity without requiring uniform complex processing across the entire graph.
3Loss of information
If the graph displays all nodes and connections, then the completeness of data flow representation is improved, but the difficulty of identifying clusters and patterns increases
Solution Approach 1:
The graph is segmented into visually distinct clusters that are separated by spatial positioning and visual styling. This segmentation makes cluster identification easier while maintaining complete data flow representation within each cluster, as the segmentation itself becomes the detection mechanism.
Solution Approach 2:
Different clusters are assigned distinct visual attributes such as color, shading, or border styling. This visual differentiation enables users to easily identify and distinguish between different clusters and patterns in the data flow graph while maintaining complete information about all nodes and connections.
Data Source
AI summary
An embodiment of the invention provides a method of displaying a data flow, wherein a description of a data flow application to be displayed is received. The data flow application includes nodes and edges connecting the nodes, wherein the nodes represent operators and the edges represent data connections for data flowing between the operations. A reason that a user is to view the data flow and/or a user constraint on a complexity of the data flow application to be displayed is determined with a processor; and, the time required to render a display of the data flow application is estimated. A transformed representation of the data flow application is created with the processor. The transformed representation is created based upon the user reason, the user constraint, the estimated time of rendering, and/or a layout strategy. The transformed representation is displayed on a graphical user interface.