Dynamic Data Stream Visualization Using Eigenspace Projection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing technologies face challenges in effectively visualizing large and dynamic data streams, particularly in time-sensitive applications, due to the unpredictable and unbounded nature of data stream information, which can overwhelm visualization tools and require full re-computation for every update.
Innovation Solution
The implementation of adaptive and incremental visualization techniques, including data stratification and eigenvector-based projection, which allow for dynamic visualization of data streams by reducing dimensionality and using sliding windows to update visualizations without re-processing the entire dataset, utilizing methods like wavelet decomposition and multidimensional scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional visualization tools are used to process data streams, then complete data analysis can be achieved, but processing time becomes excessively long and the system cannot handle real-time updates
Solution Approach 1:
The patent segments the data stream processing into multiple windows (e.g., first window and second window) where each window is processed independently. This allows parallel processing of different data portions, significantly improving processing speed while maintaining overall visualization accuracy through the combination of multiple segmented results.
Solution Approach 2:
The patent performs preliminary processing by pre-computing visualization results for the first window of data before the second window arrives. This preliminary action allows the system to prepare visualization data in advance, reducing the time needed for real-time updates and improving overall processing productivity without sacrificing accuracy.
2Measurement precision
If the entire dataset is re-processed for every update, then visualization accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The patent divides the dataset into multiple windows and processes only the new or changed portions in each window rather than re-processing the entire dataset. This segmentation approach maintains visualization accuracy by ensuring each segment is properly processed while dramatically reducing the time loss associated with full re-processing.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of the data (the current window) rather than the entire dataset. This partial processing approach maintains sufficient visualization accuracy for the current state while minimizing the time investment required, avoiding the excessive action of complete re-processing.
3Speed
If data streams are processed continuously at high rate, then real-time visualization is achieved, but system resources become overwhelmed
Solution Approach 1:
The patent segments the continuous data stream into discrete windows that can be processed at manageable rates. This segmentation reduces system resource requirements by allowing the system to handle smaller, controlled portions of data at a time while still achieving real-time visualization through the continuous processing of successive segments.
Solution Approach 2:
The patent implements periodic processing by handling data in regular windows or batches rather than continuously processing every incoming data point. This periodic action allows the system to maintain real-time visualization capabilities while reducing peak resource requirements, as the system processes data at regular intervals rather than being overwhelmed by continuous high-rate input.
Data Source
AI summary
One embodiment of the present invention includes a data communication subsystem to receive a data stream, and a data processing subsystem responsive to the data communication subsystem to generate a visualization output based on a group of data vectors corresponding to a first portion of the data stream. The processing subsystem is further responsive to a change in rate of receipt of the data to modify the visualization output with one or more other data vectors corresponding to a second portion of the data stream as a function of eigenspace defined with the group of data vectors. The system further includes a display device responsive to the visualization output to provide a corresponding visualization.