Interactive Table Multi-Level Data Relationship Visualization
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Solution Overview
Problem
Tabular data presentation in high-density datasets is limited by the inability to effectively visualize multi-level relationships between data items, as existing in-line table controls only allow manual selection and re-arrangement, failing to provide comprehensive relationship insights.
Innovation Solution
An interactive table system that allows users to select data items, identify related items, and visualize multi-level relationships by modifying the table structure dynamically based on user selections, enabling the display of relationships between selected and related data items across multiple columns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional tabular representation is used to present high-density datasets, then data can be organized in rows and columns, but the ability to visualize multi-level relationships between data items is lost
Solution Approach 1:
The patent segments the table structure by introducing collapsible/expandable column groups that can be independently controlled. Users can collapse unrelated columns and expand relevant ones to reveal multi-level relationships without overwhelming the entire table structure. This segmentation allows relationship visualization while maintaining manageable complexity through hierarchical organization of data columns.
Solution Approach 2:
The patent adds a hierarchical dimension to the traditional two-dimensional table by implementing nested column groups with multiple levels of expansion and collapse. This transforms the flat table structure into a multi-dimensional hierarchy where users can navigate through levels of data organization, revealing relationships between data items across different hierarchical levels without increasing the physical table complexity.
2Ease of operation
If in-line table controls are used to allow users to filter and re-arrange table information, then manual selection of data items is enabled, but comprehensive relationship insights between data items are not provided
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically analyzes user selection patterns and dynamically adjusts the table display to highlight and expand column groups containing related data items. When users select or interact with specific data items, the system provides feedback by automatically expanding relevant hierarchical levels and grouping related columns together, transforming manual selection into automated relationship discovery without requiring complex user operations.
Solution Approach 2:
The patent enables the table system to automatically perform relationship analysis and reorganization based on user interactions. Instead of requiring users to manually filter and re-arrange data to find relationships, the system self-adjusts by automatically expanding collapsible column groups that contain related data items, providing comprehensive relationship insights through automated adaptation to user needs rather than manual manipulation.
3Loss of information
If all data items are displayed in the table, then complete data coverage is achieved, but the ability to focus on specific relationships and reduce visual complexity is reduced
Solution Approach 1:
The patent transforms the static table display into a dynamic structure where column groups can be automatically expanded or collapsed based on user interactions and data relationships. The table adapts its display state dynamically - starting with a condensed view that maintains data coverage through hierarchical organization, then automatically expanding specific column groups to reveal relationships when users interact with data items, thus maintaining both completeness and interpretability across different interaction states.
Data Source
AI summary
In one embodiment, a first selection of a data item in an interactive table is received. Further, one or more data items related to the first selected data item in the interactive table are identified. The interactive table is modified to render the data items identified as related to the first selected data item. Furthermore, a second selection of a data item within the data items identified as related to the first selected data item is received and one or more data items related to the second selected data item within the data items identified as related to the first selected data item are identified. Further, the previously modified interactive table is again modified to render the data items identified as related to the second selected data item.