Hierarchical Time-Series Graphs for Waste Data Visualization
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Solution Overview
Problem
Existing techniques for visualizing materials' time-series data in waste management facilities are difficult to understand and require extensive space, making it challenging to accurately manage and project waste diversion rates effectively.
Innovation Solution
A method and system for visualizing data using hierarchically related graphs with a common time-series axis, displayed in a linear arrangement, which automatically updates based on user selections, allowing for precise breakdowns and comparisons of categories and sub-categories, facilitating intuitive data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing techniques are used to visualize materials' time-series data with categorical breakdowns, then comprehensive data coverage is achieved, but the visualization becomes difficult to understand and requires extensive space
Solution Approach 1:
The patent segments the hierarchical data into multiple levels (e.g., material type, sub-category, specific material) and displays them as a series of connected graphs rather than attempting to show all categories simultaneously. Each graph represents one hierarchical level, allowing comprehensive data coverage while maintaining a compact visual layout that fits on a single screen.
Solution Approach 2:
The patent introduces a hierarchical dimension to organize the data visually. Instead of spreading categories horizontally across a large space, the hierarchy is expressed vertically through stacked graphs, where each graph represents a different hierarchical level. This dimensional reorganization allows comprehensive data presentation in a compact area.
2Loss of information
If existing techniques are used to visualize materials' time-series data with categorical breakdowns, then comprehensive data coverage is achieved, but the visualization is difficult to understand
Solution Approach 1:
By segmenting the hierarchical data into discrete, manageable graph components, each representing a specific hierarchical level, the patent makes the data easier to understand. Users can focus on one level at a time while the hierarchical relationships are clearly indicated through visual connections between graphs, reducing cognitive load compared to trying to comprehend all categories simultaneously.
Solution Approach 2:
The patent uses consistent color coding across the hierarchical graphs to represent the same categories at different levels. This visual consistency helps users quickly understand relationships and comparisons across hierarchical levels, significantly improving understandability while maintaining comprehensive data coverage.
3Productivity
If hierarchical graphs are displayed with a common time-series axis in linear arrangement, then data comparison efficiency is improved, but the system complexity increases due to automatic updates based on user selections
Solution Approach 1:
The patent implements a universal graph structure that can represent any hierarchical level with the same visual format and time axis. This multi-functional design allows the same graph template to display different hierarchical levels (material types, sub-categories, specific materials) interchangeably, improving data comparison efficiency while managing system complexity through reuse of proven components.
Solution Approach 2:
The system incorporates feedback mechanisms where user selections at one hierarchical level automatically trigger updates to the corresponding graphs. This feedback loop allows the system to dynamically adjust the displayed data based on user interests, improving comparison efficiency for relevant data while the modular architecture manages the complexity of these automatic updates.
Data Source
AI summary
Methods and systems for visualizing data include forming hierarchically related graphs having a common time-series axis. The hierarchically related graphs are displayed in a linear arrangement, such that shared values on the common time-series axis align for each graph. The hierarchically related graphs are automatically updated in accordance with a user selection of an element in a data hierarchy by removing graphs below a lowest-order common ancestor in the data hierarchy between the user selection and a previously displayed selection and replacing the removed graphs with new graphs that reflect the user selection.