Time Series Data Visualization Using Importance-Based Tree Structures
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Solution Overview
Problem
Displaying large datasets, such as sales data from international corporations, is challenging due to the difficulty in interpreting the significance of various data points, as existing methods do not effectively differentiate between important and less important data.
Innovation Solution
A system that graphically represents time series data using a tree structure, where the importance of data is emphasized through varying rectangle sizes and locations, with algorithms determining importance values based on averages, medians, or other calculations, and aggregating these values to display on a display surface, allowing users to select layout masks and aggregation functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is displayed in traditional formats, then all data points are visible, but it becomes difficult to interpret the significance of various data points
Solution Approach 1:
The patent applies local quality by varying the visual properties (size, position, color) of rectangular regions according to their importance values. More important data points are represented by larger rectangles positioned in prominent locations, while less important data points use smaller rectangles in less prominent positions. This creates a visual hierarchy that maintains information completeness while dramatically improving interpretability.
Solution Approach 2:
The patent replaces traditional mechanical data presentation methods (uniform tables, charts) with an information-theoretic approach using Shannon entropy to calculate importance values. This substitution transforms the data visualization from a uniform mechanical display to an adaptive information-based representation that automatically highlights significant data points.
2Measurement precision
If importance values are calculated for all data points, then data significance can be determined, but processing time and computational complexity increase
Solution Approach 1:
The patent implements partial action by calculating importance values selectively rather than uniformly for all data points. The system uses aggregation functions to compute importance at appropriate levels of the data hierarchy, avoiding redundant calculations. This approach determines data significance with sufficient precision while minimizing unnecessary computational overhead.
3Ease of operation
If visual differentiation is applied to emphasize important data, then data interpretation is improved, but the display complexity increases
Solution Approach 1:
The patent applies asymmetry by using non-uniform rectangular regions with varying sizes and positions to represent different data points. The display deliberately breaks the symmetry of traditional uniform data presentations by positioning more important data points in asymmetric, visually prominent locations while using asymmetric size variations to encode importance levels, thereby improving interpretation without excessive complexity.
4Adaptability or versatility
If data is aggregated using multiple functions, then analysis capability is enhanced, but the system complexity increases
Solution Approach 1:
The patent implements universality by designing a multi-functional importance calculation system that can apply different aggregation functions (sum, average, maximum, minimum, custom functions) through a unified framework. The same core architecture supports multiple analysis capabilities by simply changing the aggregation function parameter, avoiding the need for separate complex systems for each analysis type and enhancing versatility without proportional complexity increase.
Data Source
AI summary
A method comprises determining an importance value for each series data set among a plurality of series data sets and visually representing each of the series data sets in accordance with the importance values.