Helical Pixel Visualization for Time Series Pattern Detection
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Solution Overview
Problem
Existing pixel-based representations of time series data lack effective visualization methods to convey temporal patterns and anomalies in a three-dimensional space, making it difficult to detect periodic patterns and trends in large volumes of data.
Innovation Solution
The use of helical pixel representations, where each pixel represents a measurement interval and its color indicates the value, allows for visualizing time series data in a three-dimensional space using helices, tuplex, and supertuplex structures, enabling users to interact and identify periodic patterns by aligning time intervals and distinguishing depth for faster comprehension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If time series data is represented in a two-dimensional pixel calendar tree, then the data can be subdivided into calendar elements for visualization, but it becomes difficult to detect periodic patterns and trends in large volumes of data
Solution Approach 1:
The patent transforms the traditional two-dimensional pixel calendar tree into a three-dimensional helical structure. Each pixel is positioned in 3D space with coordinates (x, y, z) where z represents the time dimension. This dimensional expansion allows periodic patterns to be visualized as repeating structures along the helical path, making trend detection and temporal analysis significantly easier while maintaining the pixel-based efficient representation.
2Productivity
If traditional pixel-based representations are used, then large data sets can be represented graphically efficiently, but effective visualization methods to convey temporal patterns and anomalies in three-dimensional space are lacking
Solution Approach 1:
By adding the third dimension (z-axis representing time) to the traditional 2D pixel representation, the patent creates a helical structure where temporal patterns naturally emerge as visual repetitions. This maintains graphical efficiency while dramatically improving pattern detection ease, as periodic anomalies appear as repeating visual motifs along the helical path rather than scattered points in 2D space.
Solution Approach 2:
The patent uses a helical (curved) arrangement instead of a linear or grid-based 2D layout. The helical path wraps around an axis, creating a curved spatial organization that naturally groups temporally related data points together. This curvature enables intuitive visualization of periodic patterns as repeating segments along the helical trajectory, making anomaly detection more straightforward.
3Loss of time
If helical pixel representations are used to visualize time series data in three-dimensional space, then periodic patterns can be detected faster, but the device or method complexity increases
Solution Approach 1:
The patent implements a 3D helical coordinate system where pixels are positioned according to (x, y, z) coordinates. The helical structure is mathematically defined by parametric equations that map time-series indices to 3D positions. This structured approach, while adding dimensional complexity, provides systematic methods for rendering and querying the helical space, enabling faster pattern detection through visual inspection of repeating structures along the helical path.
Data Source
AI summary
Example embodiments relate to providing pixel-based visualizations of time series data using nested helices. In example embodiments, helix portions in the time series data may be identified according to a measured time interval, where each of the helix portions represents the measured time interval in the time series data. A helical time period may then be determined and used as a helical revolution in a helical pixel representation. At this stage, the helical pixel representation may be generated using the helix portions, where proximate helix portions along a common line parallel to an axis of the helical pixel representation are chronologically separated by the helical time period.