BORA: A Personalized Data Display for Large-scale Experiments

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. Such systems commonly comprise multiple interacting parts: experiment and process control. There are multiple processing stages that record process data and derive information. And there is a component that communicates and displays the evaluated information to the operators. As the process control and data acquisition stages are usually specific to the experiment, it is common for researches to also build custom monitoring systems in unison with their data acquisition, leading to poor reusability for other experiments or future upgrades. This paper presents BORA (personalized collaBORAtive data display), a lightweight browser-based monitoring frontend that supports diverse data sources and is built specifically for customizable visualization of complex data, standardized via video streaming. It is shown how absolute positioning layout and visual overlay background can address the diverse data display design requirements. Integration of Jupyter Notebooks as part of the ecosystem addresses limitations of static web-based frameworks, providing a foundation to leverage scripting capabilities and integrate popular AI frameworks. Video streaming protocols like HLS, WebRTC, and MPEG-Websocket are used to forward visual outputs of remote processing and imaging pipelines of an experiment. The study explores the implications for these use cases, highlighting its potential to transform data visualization and decision-making processes.