BORA: A Personalized Data Display for Large-scale Experiments

TL;DR

BORA is a customizable, lightweight, browser-based data display system designed for real-time monitoring of large-scale physics experiments, supporting diverse protocols and integrating AI tools for enhanced data visualization.

Contribution

Introduces BORA, a flexible, standardized visualization platform with video streaming and AI integration, addressing limitations of traditional standalone monitoring solutions.

Findings

Supports multiple communication protocols

Enables real-time, customizable data visualization

Integrates AI frameworks for advanced analysis

Abstract

Given the rapid improvement of the detectors at high-energy physics experiments, the need for real-time data monitoring systems has become imperative. The significance of these systems lies in their ability to display experiment status, steer software and hardware instrumentation, and provide alarms, thus enabling researchers to manage their experiments better. However, researchers typically build most data monitoring systems as standalone in-house solutions that cannot be reused for other experiments or future upgrades. We present BORA (personalized collaBORAtive data display), a lightweight browser-based monitoring system that supports diverse protocols and is built specifically for customizable visualization of complex data, which we standardize via video streaming. We show how absolute positioning layout and visual overlay background can address the diverse data display design requirements. Using the client-server architecture, we enable support for diverse communication protocols, with the server component responsible for parsing the incoming data. We integrate the Jupyter Notebook as part of our ecosystem to address the limitations of the web-based framework, providing a foundation to leverage scripting capabilities and integrate popular AI frameworks. Since video streaming is a core component of our framework, we evaluate viable approaches to streaming protocols like HLS, WebRTC, and MPEG-Websocket. The study explores the implications for our use case, highlighting its potential to transform data visualization and decision-making processes.