Research Topic

Operational Intelligence

About this Research Topic

High energy physics (HEP) experiments are controlled and monitored through a diverse set of tools and infrastructures that, in their turn, are producers of large amounts of data. These data are mainly characterized by large volumes and large variety, being generated by multiple uneven data sources (sensors meters, service logs, alarm states), with sometimes fragmentary or irregular patterns.
As the experiments' complexity increases, a wide set of tools is required to streamline their operation and data collection.
This includes real-time dynamic decisions, a prompt identification of system’s faults, anomalies and inefficiencies in order to sustain high performance for these systems. In addition, it includes maintaining experimental and computing infrastructures sustainably and increasing their reliability.

In the context of this discussion, we define Operational Intelligence (OI) as the collection of activities that involve extracting information from a large number of monitored data sources and actively apply analytical insights exploiting machine learning methods.
The focus of this article collection is on contributions from the HEP community which currently explore this area of research. We welcome contributions from a wide range of technical subjects and applications, which can be classified into the following main areas:

• Machine learning-based optimization pipelines, including performance benchmarking with traditional tools, and Infrastructure sustainability.
• Advances on reduced tuning time of HEP experiments infrastructures.
• Developments of anomaly detections and failure prediction.
• Applications of machine learning in monitoring and controlling experiments and computing infrastructures.


Keywords: Operational Intelligence, HEP, Machine Learning, Information Extraction, Data Sources, Experiments infrastructures, Anomaly detection, Failure prediction, Computing infrastructures, High Performance


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

High energy physics (HEP) experiments are controlled and monitored through a diverse set of tools and infrastructures that, in their turn, are producers of large amounts of data. These data are mainly characterized by large volumes and large variety, being generated by multiple uneven data sources (sensors meters, service logs, alarm states), with sometimes fragmentary or irregular patterns.
As the experiments' complexity increases, a wide set of tools is required to streamline their operation and data collection.
This includes real-time dynamic decisions, a prompt identification of system’s faults, anomalies and inefficiencies in order to sustain high performance for these systems. In addition, it includes maintaining experimental and computing infrastructures sustainably and increasing their reliability.

In the context of this discussion, we define Operational Intelligence (OI) as the collection of activities that involve extracting information from a large number of monitored data sources and actively apply analytical insights exploiting machine learning methods.
The focus of this article collection is on contributions from the HEP community which currently explore this area of research. We welcome contributions from a wide range of technical subjects and applications, which can be classified into the following main areas:

• Machine learning-based optimization pipelines, including performance benchmarking with traditional tools, and Infrastructure sustainability.
• Advances on reduced tuning time of HEP experiments infrastructures.
• Developments of anomaly detections and failure prediction.
• Applications of machine learning in monitoring and controlling experiments and computing infrastructures.


Keywords: Operational Intelligence, HEP, Machine Learning, Information Extraction, Data Sources, Experiments infrastructures, Anomaly detection, Failure prediction, Computing infrastructures, High Performance


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

01 June 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

01 June 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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