Hierarchical Data Navigation Using Climb Dive Spin Inputs
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Solution Overview
Problem
Visualizing and navigating large datasets is challenging as existing methods either provide too much data with little context or too much context with insufficient detail, failing to effectively convey both simultaneously.
Innovation Solution
A navigation mechanism using climb/dive and rotate inputs to traverse hierarchical datasets, which can be rendered with a combination of context and detail sections, allowing users to explore datasets intuitively and efficiently by displaying the focus node's context and details of nearby nodes in various formats, such as zipper trees or filial-heir arrangements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a visualization scheme presents vast amount of data, then the quantity of information displayed is improved, but the detail and context understanding deteriorates
Solution Approach 1:
The hierarchical dataset is segmented into multiple levels of detail, with each level representing a different granularity of data. Users can navigate between levels to view either broad context or specific details, resolving the contradiction between displaying vast amounts of data and maintaining understanding of context and detail.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data visualization, organizing data in parent-child relationships across multiple levels. This additional dimension allows users to traverse from high-level summaries to detailed views without losing context, as each level provides a different perspective on the same dataset.
2Measurement precision
If a visualization scheme presents details of a large dataset, then the detail understanding is improved, but the overall context understanding deteriorates
Solution Approach 1:
The visualization employs a nested structure where detailed views are contained within broader contextual frameworks. When users examine detailed information at lower hierarchical levels, the parent-level context remains accessible, allowing users to maintain understanding of both specific details and overall structure simultaneously.
Solution Approach 2:
By adding the hierarchical level as an additional dimension, the system allows users to navigate between detail and context without losing either. The hierarchical structure creates a multi-layered view where detailed information exists within the broader context of parent levels, resolving the trade-off between detail precision and contextual understanding.
3Ease of operation
If conventional navigation methods are used for hierarchical data, then the ease of operation is improved for simple datasets, but the productivity and efficiency deteriorate for deep and complex datasets
Solution Approach 1:
The navigation mechanism provides universal controls that work across all hierarchical levels and dataset complexities. The same climb/dive and spin operations function effectively whether navigating shallow or deep hierarchies, simple or complex datasets, maintaining ease of operation while improving productivity through consistent, level-independent navigation.
Solution Approach 2:
The navigation system dynamically adapts to the hierarchical structure being traversed. The climb/dive mechanism automatically adjusts to the current level's parent-child relationships, and the spin mechanism adapts to the number of siblings at each level. This dynamic behavior maintains ease of operation across varying dataset complexities while improving navigation efficiency through automated level-aware traversal.
Data Source
AI summary
A navigation mechanism for hierarchical data uses a climb/dive and rotate inputs to traverse a hierarchical dataset. The hierarchical dataset may be arranged in a filial-heir arrangement for easy traversal. The navigation mechanism may be used with several different ways of displaying the data, some of which may display both overall context and detailed view of the data. One such example may have a context section and a detail section, where the context section may present the overall context of a focus node in relation to the overall dataset and the detail section may display other nodes that are at the same or lower level than the focus node.