Real-Time Streaming Data Visualization System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems face significant challenges in efficiently analyzing and visualizing high-volume live data streams in real-time, leading to performance issues and data I/O lag, especially with conventional preload-store-plot approaches and front-end visualization libraries that struggle to scale with large data sets.
Innovation Solution
A visualization system is developed with a distributed architecture, utilizing a rendering engine that leverages local Graphics Processing Units (GPUs) for accelerated rendering, a flexible temporal and geospatial framework, and adaptive jitter stabilization to handle large-scale, real-time streaming data, enabling interactive and dynamic visualization across multiple linked views and virtual reality environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional preload-store-plot approaches are used to visualize high-volume data streams, then data visualization can be achieved, but data I/O lag increases and real-time performance deteriorates
Solution Approach 1:
The system segments the visualization pipeline into separate components: data ingestion, processing, and rendering. The front end is separated from the back end, allowing independent optimization. Data is processed in chunks and rendered incrementally, reducing I/O wait times while maintaining real-time performance.
Solution Approach 2:
An intermediary data structure layer is introduced between the raw data stream and the visualization rendering. This intermediary layer pre-processes and buffers data in an optimized format, reducing the I/O bottleneck during rendering while maintaining real-time update capabilities.
2Ease of operation
If front-end visualization libraries are used to handle large data sets, then expressiveness and ease of programming are improved, but scaling performance deteriorates when data points grow too large
Solution Approach 1:
The patent extracts the heavy lifting of large-scale data processing from the front-end visualization library and moves it to the back-end processing system. The front end retains only the essential visualization rendering, while the back end handles data aggregation, filtering, and transformation, enabling scaling to millions of data points.
Solution Approach 2:
The system introduces an additional processing dimension by implementing a multi-layered architecture with back-end data processing, intermediate data structures, and front-end rendering. This dimensional expansion allows the system to handle large data sets by distributing processing across multiple layers rather than relying on a single front-end library.
3Productivity
If Level-Of-Detail scaling is applied to mitigate performance issues, then some performance improvement is achieved, but the ability to display multiple linked views is compromised
Solution Approach 1:
The back-end processing system is designed to be universal and support multiple visualization types and linked views simultaneously. Data is processed and structured in a way that can be efficiently rendered across multiple views without requiring separate processing pipelines, maintaining both performance and versatility.
Data Source
AI summary
Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program, and a method for rendering interactive visualizations of high volume streaming data. Streaming data that describes a plurality of events is loaded in memory. The streaming data includes geospatial data that specifies a source location and a destination location for each event. Visual metaphors representing the events are rendered using a visualization mathematical model that defines, for each visual metaphor, a spatial position and opacity for each point along the visual metaphor throughout a sliding time window.