The Alice data quality monitoring system

A. Telesca, B. Von Haller, S. Chapeland, F. Carena, W. Carena, V. Chibante Barroso, F. Costa, R. Divià, E. Denes, U. Fuchs, G. Simonetti, P. Vande Vyvre

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

ALICE (A Large Ion Collider Experiment) is a heavy-ion detector designed to study the physics of strongly interacting matter and the quark-gluon plasma at the CERN Large Hadron Collider (LHC). Due to the complexity of ALICE in terms of number of detectors and performance requirements, data quality monitoring (DQM) plays an essential role in providing an online feedback on the data being recorded. It intends to provide operators with precise and complete information to quickly identify and overcome problems, and, as a consequence, to ensure acquisition of high quality data. DQM typically involves the online gathering of data samples, their analysis by user-defined algorithms and the visualization of the monitoring results. In this paper, we illustrate the final design of the DQM software framework of ALICE, AMORE (Automatic Monitoring Environment), and its latest features and developments. We describe how this system is used to monitor the event data coming from the ALICE detectors allowing operators and experts to access a view of monitoring elements and to detect potential problems. Important features include the integration with the offline analysis and reconstruction framework and the interface with the electronic logbook that makes the monitoring results available everywhere through a web browser. Furthermore, we show the advantage of using multi-core processors through a parallel images/results production and the flexibility of the graphic user interface that gives to the user the possibility to apply filters and customize the visualization. We finally review the wide range of usage people make of this framework, from the basic monitoring of a single sub-detector to the most complex ones within the High Level Trigger farm or using the Prompt Reconstruction. We also describe the various ways of accessing the monitoring results. We conclude with our experience, after the LHC restart, when monitoring the data quality in a realworld and challenging environment.

Original languageEnglish
Title of host publicationConference Record - 2010 17th IEEE-NPSS Real Time Conference, RT10
DOIs
Publication statusPublished - Dec 1 2010
Event2010 17th IEEE-NPSS Real Time Conference, RT10 - Lisbon, Portugal
Duration: May 24 2010May 28 2010

Publication series

NameConference Record - 2010 17th IEEE-NPSS Real Time Conference, RT10

Other

Other2010 17th IEEE-NPSS Real Time Conference, RT10
CountryPortugal
CityLisbon
Period5/24/105/28/10

    Fingerprint

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Telesca, A., Von Haller, B., Chapeland, S., Carena, F., Carena, W., Barroso, V. C., Costa, F., Divià, R., Denes, E., Fuchs, U., Simonetti, G., & Vande Vyvre, P. (2010). The Alice data quality monitoring system. In Conference Record - 2010 17th IEEE-NPSS Real Time Conference, RT10 [5750364] (Conference Record - 2010 17th IEEE-NPSS Real Time Conference, RT10). https://doi.org/10.1109/RTC.2010.5750364