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Designing a future Conditions Database based on LHC experience
Author(s) -
D. Barberis,
A. Formica,
E. J. Gallas,
G. Govi,
Giorgia Miotto,
A. Pfeiffer
Publication year - 2015
Publication title -
journal of physics conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/664/4/042015
Subject(s) - large hadron collider , computer science , database , atlas (anatomy) , data science , data mining , particle physics , medicine , physics , anatomy
Starting from the experience collected by the ATLAS and CMS experiments in handling condition data during the first LHC run, we present a proposal for a new generation of condition databases, which could be implemented by 2020. We will present the identified relevant data flows for condition data and underline the common use cases that lead to a joint effort for the development of a new system.Condition data is needed in any scientific experiment. It includes any ancillary data associated with primary data taking such as detector configuration, state or calibration or the environment in which the detector is operating. Condition data typically reside outside the primary data store for various reasons (size, complexity or availability) and are best accessed at the point of processing or analysis (including for Monte Carlo simulations). The ability of any experiment to produce correct and timely results depends on the complete and efficient availability of needed conditions for each stage of data handling. Therefore, any experiment needs a condition data architecture which can not only store conditions, but deliver the data efficiently, on demand, to potentially diverse and geographically distributed set of clients. The architecture design should consider facilities to ease conditions management and the monitoring of its conditions entry, access and usage.

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