Data Visualization System for Mass Event Latency Reduction
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Solution Overview
Problem
Existing computing systems for managing mass participation events face challenges such as high system load times, data latency, and limited network connectivity, which hinder real-time data visualization and participant tracking, especially during large events like marathons.
Innovation Solution
A dynamic data visualization system that processes unique data streams for each type of information, uses prediction logic with machine-learning mechanisms to simulate event scenarios, and continuously refines predictions with real-time data validation, enabling fast and seamless data updates and precise predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a computing system processes data from multiple sources simultaneously for mass participation events, then the quantity of information available improves, but system load time increases
Solution Approach 1:
The system segments data processing by creating unique data streams for each type of information (participant data, resource data, environmental data). Each data stream is processed independently through dedicated communication channels, allowing parallel processing without mutual interference, thus reducing overall system load time while maintaining comprehensive information availability.
Solution Approach 2:
The system transitions from sequential single-channel processing to multi-dimensional parallel processing by establishing separate communication channels for each data type. This dimensional expansion allows simultaneous data ingestion across multiple streams, resolving the trade-off between information completeness and processing speed.
2Speed
If the system processes all event data in real-time, then data freshness improves, but computational complexity increases
Solution Approach 1:
The system divides complex event data into distinct segmented streams (participant information, resource status, environmental conditions). Each stream is processed through simplified dedicated pipelines rather than a single complex processing system, reducing overall computational complexity while maintaining real-time processing capability.
3Loss of information
If the system monitors all participants and resources continuously, then situational awareness improves, but network bandwidth consumption increases
Solution Approach 1:
The system segments monitoring data into specialized streams based on information type and priority. Critical safety-related data receives continuous high-bandwidth monitoring, while less critical data uses reduced bandwidth transmission. This segmented approach maintains comprehensive situational awareness while optimizing network resource utilization.
Data Source
AI summary
Aspects of the present disclosure relate to data visualization, and more specifically, to technology that automatically visualizes various analytics and predictions generated for mass participation endurance events, or other mass participation events of interest.