Radial Causality Visualization for Network Event Analysis
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Solution Overview
Problem
Complex data sets in computing environments make it difficult for network managers to assess and understand the impact of changing events on their systems and customers, as existing visualizations often mask technical details with simple statistics.
Innovation Solution
A radial causality visualization method that plots data as nodes on concentric shapes, using connecting lines to represent causal relationships, allowing for a hierarchical and interactive display of data, enabling users to track state changes and events through a flexible event-driven framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple statistics and summaries are used to present data, then ease of operation is improved, but loss of information worsens
Solution Approach 1:
The patent segments complex data into hierarchical levels (root causes, intermediate causes, effects) and visualizes them as separate nodes in a causality graph. This allows managers to view summaries at higher levels while drilling down into technical details at lower levels, resolving the contradiction between simplicity and information completeness.
Solution Approach 2:
The patent adds a visual dimension to data presentation by using spatial positioning of nodes and connecting lines to represent causal relationships. This transforms flat statistics into a multi-dimensional causality graph that preserves technical details while maintaining visual clarity and ease of interpretation.
2Loss of information
If comprehensive data is presented to satisfy technical managers, then loss of information is reduced, but device complexity worsens
Solution Approach 1:
The system segments comprehensive data into modular nodes representing distinct causal elements. Each node can be independently configured and visualized, allowing the system to handle complex data without creating an overwhelming monolithic display. Managers can focus on specific segments of the causality graph as needed.
Solution Approach 2:
The causality graph is implemented as a dynamic, interactive visualization that adapts to user needs. Nodes and connections can be expanded, collapsed, or highlighted based on user interaction, allowing the system to present comprehensive data in a controlled, manageable manner that doesn't overwhelm users with static complexity.
3Measurement precision
If hierarchical data structure is used to show causal relationships, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The patent segments causal relationships into discrete directed edges connecting nodes in a hierarchy. This segmentation allows precise representation of causal links while simplifying the processing logic, as each relationship can be independently analyzed and visualized without requiring complex global processing.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw hierarchical data into the causality graph structure. This intermediary layer handles the complexity of data processing internally, presenting a simplified precise view to users while managing the computational complexity behind the scenes.
Data Source
AI summary
A method and associated apparatus for generating a radial causality visualization including accessing a data store storing a first set of data and a second set of data, the first set of data and the second set of data being in a predetermined hierarchical relationship with each other, plotting the first set of data as nodes disposed on a first shape (e.g., circle, ring, rectangle, etc.) of a plurality of concentric shapes of a displayed visualization, plotting the second set of data as nodes on a second shape of the plurality of concentric shapes of the displayed visualization, wherein the second shape is disposed radially further outward from a center of the plurality of concentric shapes compared to the first shape, and displaying on the displayed visualization respective connecting lines that connect nodes disposed on the first shape with nodes disposed on the second shape.