9-13 July 2018
Sofia, Bulgaria
Europe/Sofia timezone

Improving the use of data quality metadata via a partnership of technologies and resources between the CMS experiment at CERN and industry

10 Jul 2018, 12:15
Hall 3.1 (National Palace of Culture)

Hall 3.1

National Palace of Culture

presentation Track 1 - Online computing T1 - Online computing


Virginia Azzolini (Massachusetts Inst. of Technology (US))


The CMS experiment dedicates a significant effort to supervise the quality of its data, online and offline. A real-time data quality (DQ) monitoring is in place to spot and diagnose problems as promptly as possible to avoid data loss. The evaluation a posteriori of processed data is designed to categorize the data in term of their usability for physics analysis. These activities produce DQ metadata.
The DQ evaluation relies on a visual inspection of monitoring features. This practice has a high cost in term of human resources and is naturally subject to human arbitration. Potential limitations are linked to the ability to spot a problem within the overwhelming number of quantities to monitor, or to the understanding of detector evolving conditions.
In view of Run III, CMS aims at integrating deep learning technique in the online workflow to promptly recognize and identify anomalies and improve DQ metadata precision.
The CMS experiment engaged in a partnership with IBM with the objective to support, with automatization, the online operations and to generate benchmarking technological results. The research goals, agreed within the CERN Openlab framework, how they matured in a demonstration application and how they are achieved, through a collaborative contribution of technologies and resources, will be presented.

Primary authors

Adrian Alan Pol (Université Paris-Saclay (FR)) Colin Jessop (University of Notre Dame (US)) Gianluca Cerminara (CERN) Jean-Roch Vlimant (California Institute of Technology (US)) Maurizio Pierini (CERN) Michael Andrews (Carnegie-Mellon University (US)) Nabarun Dev (University of Notre Dame (US)) Nancy Marinelli (University of Notre Dame (US)) Tanmay Mudholkar (Carnegie-Mellon University (US)) Virginia Azzolini (Massachusetts Inst. of Technology (US))

Presentation Materials