电邮这篇文章 Evaluation of the Level-of-Detail Generator for Visual Analysis of the ATLAS Computing Metadata
- 作者: Grigorieva M.1, Titov M.1, Alekseev A.1, Artamonov A.2, Klimentov A.3, Korchuganova T.1, Milman I.2, Galkin T.2, Pilyugin V.2
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隶属关系:
- Lomonosov Moscow State University
- National Research Nuclear University MEPhI
- Brookhaven National Laboratory
- 期: 卷 40, 编号 11 (2019)
- 页面: 1788-1798
- 栏目: Article
- URL: https://journals.rcsi.science/1995-0802/article/view/206052
- DOI: https://doi.org/10.1134/S199508021911012X
- ID: 206052
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详细
The ATLAS experiment at the LHC processes, analyses and stores vast amounts of data, which is either recorded by the detector or simulated worldwide using Monte Carlo methods. ATLAS Computing metadata is generated at very high rates and volumes. The necessity to analyze this metadata is constantly increasing, since the heterogeneous, distributed and dynamically changing computing infrastructure requires sophisticated optimization decisions, made by human or/and by machines. Visual analytics is one of the methods facilitating the analysis of massive amounts of data (structured, semi-structured, and unstructured) which leverages human judgement by means of interactive visual representations. Given the huge number of ATLAS computing jobs that need to be visualized simultaneously for error investigations or other optimization processes, resources of the client application responsible for such visualization may reach its limits. Data objects that share similar feature values can be represented and visualized as a single group, thus initial large data sample would be represented at different levels of detail. This approach will also avoid client overload. In this paper we evaluate implementations of k-means-based Level-of-Detail generator method applied to the metadata of ATLAS jobs. This method is used in the visual analytics application InVEx (Interactive Visual Explorer) that is under development, and which is based on 3-dimensional interactive visualization of multidimensional data.
作者简介
M. Grigorieva
Lomonosov Moscow State University
编辑信件的主要联系方式.
Email: maria@srcc.msu.ru
俄罗斯联邦, Moscow, 119234
M. Titov
Lomonosov Moscow State University
编辑信件的主要联系方式.
Email: mikhail.titov@cern.ch
俄罗斯联邦, Moscow, 119234
A. Alekseev
Lomonosov Moscow State University
编辑信件的主要联系方式.
Email: aaleksee@cern.ch
俄罗斯联邦, Moscow, 119234
A. Artamonov
National Research Nuclear University MEPhI
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Email: aaartamonov@mephi.ru
俄罗斯联邦, Moscow, 115409
A. Klimentov
Brookhaven National Laboratory
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Email: aak@bnl.gov
美国, Upton, NY, 11973
T. Korchuganova
Lomonosov Moscow State University
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Email: tatiana.korchuganova@cern.ch
俄罗斯联邦, Moscow, 119234
I. Milman
National Research Nuclear University MEPhI
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Email: igal.milman@gmail.com
俄罗斯联邦, Moscow, 115409
T. Galkin
National Research Nuclear University MEPhI
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Email: z@wqc.me
俄罗斯联邦, Moscow, 115409
V. Pilyugin
National Research Nuclear University MEPhI
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Email: vvpiluygin@mephi.ru
俄罗斯联邦, Moscow, 115409
