Trend Density Plot Data Visualization
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Solution Overview
Problem
Current computing technologies face challenges in effectively visualizing and analyzing data series with non-coterminous endpoints and varying sample rates, making it difficult to discern trends and patterns, especially in cyclic data sets.
Innovation Solution
The implementation of a technical computing environment (TCE) that uses trend density plots to normalize and visualize data series by establishing boundaries and regions, allowing for the identification of percentages of data points above or below these boundaries, and rendering these plots on output devices for better trend identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data visualization methods are used for data series with non-coterminous endpoints and varying sample rates, then the data can be displayed, but trend identification and pattern recognition become difficult
Solution Approach 1:
The patent segments the data series by dividing them into multiple subsets based on time periods or characteristics. Each subset is normalized independently using the same baseline, allowing for accurate trend identification across non-coterminous endpoints and varying sample rates while maintaining computational manageability
Solution Approach 2:
The patent transforms the data by changing parameters such as normalization baselines and scaling factors. By adjusting these parameters according to the specific characteristics of each data series (non-coterminous endpoints, varying sample rates), the system achieves accurate trend visualization without requiring complex preprocessing
2Adaptability or versatility
If data series are normalized to a common baseline for comparison, then trend comparison improves, but the complexity of handling varying sample rates and non-coterminous endpoints increases
Solution Approach 1:
The patent implements a universal normalization framework that can handle multiple types of data series (different sample rates, non-coterminous endpoints, cyclic patterns) using the same baseline and methodology. This multi-functional approach enables comparability across diverse data types without requiring separate complex normalization processes for each case
Solution Approach 2:
The patent performs preliminary actions by establishing common baselines and normalization rules before comparing data series. By pre-defining the normalization approach and handling edge cases in advance, the system achieves versatile data series comparison while minimizing the complexity during the actual comparison process
Data Source
AI summary
In an embodiment, a first data series and a second data series may be acquired. A first boundary may be established based on the first data series. The second boundary may be established on the second data series. A first region with respect to the first boundary may be identified. A second region with respect to the second boundary may be identified. A first scale may be associated with the first region and a second scale may be associated with the second region. A depiction of the first data series and the second data series may be rendered the depiction may include an indication of one or more of the first boundary, the second boundary, the first region, the second region, the first scale, or the second scale.