NDLI: CERN accelerator data logging and analysis

Content Provider	IEEE Xplore Digital Library
Author	Roderick, C.
Copyright Year	2013
Description	Author affiliation: CERN, Geneva, Switzerland (Roderick, C.)
Abstract	During the 10 years since it's first operational use, the scope and scale of the CERN Accelerator Logging Service (LS) has evolved significantly: from an LHC specific service expected to store 1TB / year; to a CERN-wide service spanning the complete accelerator complex (including related sub-systems and experiments) currently storing more than 50 TB / year on-line for some 1 million signals. Despite the massive increase over initial expectations the LS remains reliable, and highly usable - this can be attested to by the 5 million daily / average number of data extraction requests, from close to 1000 users. Although a highly successful service, demands on the LS are expected to increase significantly as CERN prepares LHC for running at top energy, which is likely to result in at least doubling current data volumes. Furthermore, focus is now shifting firmly towards a need to perform complex analysis on logged data, sometimes cross-correlating with data from other systems, and persisting the results. All of this must be achievable in a time and resource efficient manner, which in-turn presents new challenges. This paper gives an overview of the data logging facilities and extraction tools, describes the data analysis challenges, and the on-going activities to allow users to obtain more and more value from the substantial amounts of logged data.
Starting Page	1
Ending Page	3
File Size	484328
Page Count	3
File Format	PDF
e-ISBN	9781479905348
DOI	10.1109/NSSMIC.2013.6829573
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-27
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Large Hadron Collider Data mining Java Distributed databases Data analysis Scalability
Content Type	Text
Resource Type	Article

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