BORA: A Personalized Data Display for Large-scale Experiments
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909209325469696 |
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| author | Jerome, Nicholas Tan Chilingaryan, Suren Dritschler, Timo Kopmann, Andreas |
| author_facet | Jerome, Nicholas Tan Chilingaryan, Suren Dritschler, Timo Kopmann, Andreas |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_14397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BORA: A Personalized Data Display for Large-scale Experiments Jerome, Nicholas Tan Chilingaryan, Suren Dritschler, Timo Kopmann, Andreas Human-Computer Interaction Systems and Control 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. |
| title | BORA: A Personalized Data Display for Large-scale Experiments |
| topic | Human-Computer Interaction Systems and Control |
| url | https://arxiv.org/abs/2405.14397 |