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.

翻译：随着高能物理实验探测器的快速改进，对实时数据监控系统的需求变得日益迫切。这些系统的重要性在于其能够展示实验状态、引导软硬件仪器运行并提供警报，从而使研究人员能够更好地管理实验。然而，研究人员通常将大多数数据监控系统构建为独立的内部解决方案，无法在其他实验或未来升级中重复使用。本文提出BORA（个性化协作数据展示系统），这是一种基于浏览器的轻量级监控系统，支持多种协议，专门为复杂数据的可定制可视化而构建，我们通过视频流技术实现了标准化。我们展示了绝对定位布局和视觉叠加背景如何满足多样化的数据展示设计需求。采用客户端-服务器架构，我们实现了对多种通信协议的支持，服务器组件负责解析传入数据。我们将Jupyter Notebook集成到生态系统中，以解决基于Web框架的局限性，为利用脚本功能和集成流行AI框架提供了基础。由于视频流是我们框架的核心组成部分，我们评估了HLS、WebRTC和MPEG-Websocket等流媒体协议的可行方案。本研究探讨了该方案对我们应用场景的意义，突显了其在改变数据可视化和决策过程方面的潜力。