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• W1502783005 abstract "Experiments in high-energy astroparticle physics produce large amounts of data as continuous high-volume streams. Gaining insights from the observed data poses a number of challenges to data analysis at various steps in the analysis chain of the experiments. Machine learning methods have already cleaved their way selectively at some particular stages of the overall data mangling process. In this paper we investigate the deployment of machine learning methods at various stages of the data analysis chain in a gamma-ray astronomy experiment. Aiming at online and real-time performance, we build up on prominent software libraries and discuss the complete cycle of data processing from raw-data capturing to high-level classification using a data-flow based rapid-prototyping environment. In the context of a gamma-ray experiment, we review user requirements in this interdisciplinary setting and demonstrate the applicability of our approach in a real-world setting to provide results from high-volume data streams in real-time performance." @default.
• W1502783005 created "2016-06-24" @default.
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• W1502783005 date "2015-01-01" @default.
• W1502783005 modified "2023-10-06" @default.
• W1502783005 title "Online Analysis of High-Volume Data Streams in Astroparticle Physics" @default.
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• W1502783005 doi "https://doi.org/10.1007/978-3-319-23461-8_7" @default.
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