Multi-layered Dataset Relationship Mapping and Visualization
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Solution Overview
Problem
Conventional approaches fail to effectively analyze and visualize complex relationships among multi-layered datasets, leading to improper recognition and decision-making by users.
Innovation Solution
A system that stores original datasets, generates derivative datasets, and determines relationships among them based on analytical, similarity, modification, and user-interaction relationships, then creates a node map to visualize these connections, allowing for filtered and focused visualizations over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional approaches are used to store and visualize datasets, then simple relationships can be displayed, but complex multi-layered relationships among derivative datasets cannot be effectively analyzed or visualized
Solution Approach 1:
The system segments complex dataset relationships into hierarchical levels (original datasets, first derivative datasets, second derivative datasets) and visualizes them as separate but connected node maps. Each level can be independently analyzed while maintaining connections to other levels, preventing information loss in complex relationships.
Solution Approach 2:
The system adds a temporal dimension to the visualization by displaying node maps at different points in time and showing transitions between them. This allows users to track how relationships evolve and maintain context across multiple time points, preserving relationship information that would be lost in static visualizations.
2Loss of information
If all datasets and relationships are visualized in a single node map, then complete information is provided, but the visualization becomes too complex for users to properly recognize relationships
Solution Approach 1:
The system divides the complete relationship information into multiple focused node maps, each showing relationships at a specific hierarchical level (original, first derivative, second derivative datasets). Users can navigate between these segmented views to understand specific relationships without being overwhelmed by the complete complexity.
Solution Approach 2:
The system provides dynamic filtering capabilities that allow users to selectively display or hide different types of relationships and dataset levels in the node map. Users can adjust the visualization dynamically based on their specific analysis needs, maintaining ease of operation while preserving access to complete information when needed.
3Loss of time
If node maps are generated repeatedly at different points in time, then temporal transitions can be tracked, but the complexity of managing and presenting multiple node maps increases
Solution Approach 1:
The system implements a universal node map framework that handles multiple time points and hierarchical levels through a single integrated interface. The same visualization engine and interaction mechanisms work across all time points and dataset levels, reducing the complexity of managing multiple node maps while enabling comprehensive temporal tracking.
Data Source
AI summary
A system stores original datasets in a datastore. The system generates first derivative datasets from the original datasets, and generates second derivative datasets from at least the first derivative datasets. The system determines relationships among the original datasets, the first derivative datasets, and the second derivative datasets, based on an analytical relationship between two datasets, a similarity relationship between two datasets, a modification relationship between two datasets, and a user-interaction relationship between two datasets. Then, the system generates a node map including at least part of the original datasets, the first derivative datasets, and the second derivative datasets as a node, and at least part of the determined analytical, similarity, modification, and user-interaction relationships between two nodes as a link.