ICALEPCS2019 - List of Authors (Sciacca, E.)

Paper	Title	Page
MOPHA032	Big Data Architectures for Logging and Monitoring Large Scale Telescope Arrays	268
	A. Costa, U. Becciani, P. Bruno, A.S. Calanducci, A. Grillo, S. Riggi, E. Sciacca, F. Vitello INAF-OACT, Catania, Italy V. Conforti, F. Gianotti INAF, Bologna, Italy J. Schwarz INAF-Osservatorio Astronomico di Brera, Merate, Italy G. Tosti Università degli di Perugia, Perugia, Italy
	Funding: This work was partially supported by the ASTRI "Flagship Project" financed by the Italian Ministry of Education, University, and Research and led by the Italian National Institute of Astrophysics. Large volumes of technical and logging data result from the operation of large scale astrophysical infrastructures. In the last few years several "Big Data" technologies have been developed to deal with a huge amount of data, e.g. in the Internet of Things (IoT) framework. We are comparing different stacks of Big Data/IoT architectures including high performance distributed messaging systems, time series databases, streaming systems, interactive data visualization. The main aim is to classify these technologies based on a set of use cases typically related to the data produced in the astronomical environment, with the objective to have a system that can be updated, maintained and customized with a minimal programming effort. We present the preliminary results obtained, using different Big Data stack solution to manage some use cases related to quasi real-time collection, processing and storage of the technical data, logging and technical alert produced by the array of nine ASTRI telescopes that are under development by INAF as a pathfinder array for the Cherenkov astronomy in the TeV energy range. ASTRI Project: http://www.brera.inaf.it/~astri/wordpress/ *CTA Project: https://www.cta-observatory.org/
	Poster MOPHA032 [1.327 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA032
About •	paper received ※ 02 October 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Big Data Architectures for Logging and Monitoring Large Scale Telescope Arrays

268

A. Costa, U. Becciani, P. Bruno, A.S. Calanducci, A. Grillo, S. Riggi, E. Sciacca, F. Vitello
INAF-OACT, Catania, Italy
V. Conforti, F. Gianotti
INAF, Bologna, Italy
J. Schwarz
INAF-Osservatorio Astronomico di Brera, Merate, Italy
G. Tosti
Università degli di Perugia, Perugia, Italy

Funding: This work was partially supported by the ASTRI "Flagship Project" financed by the Italian Ministry of Education, University, and Research and led by the Italian National Institute of Astrophysics.
Large volumes of technical and logging data result from the operation of large scale astrophysical infrastructures. In the last few years several "Big Data" technologies have been developed to deal with a huge amount of data, e.g. in the Internet of Things (IoT) framework. We are comparing different stacks of Big Data/IoT architectures including high performance distributed messaging systems, time series databases, streaming systems, interactive data visualization. The main aim is to classify these technologies based on a set of use cases typically related to the data produced in the astronomical environment, with the objective to have a system that can be updated, maintained and customized with a minimal programming effort. We present the preliminary results obtained, using different Big Data stack solution to manage some use cases related to quasi real-time collection, processing and storage of the technical data, logging and technical alert produced by the array of nine ASTRI telescopes that are under development by INAF as a pathfinder array for the Cherenkov astronomy in the TeV energy range.
*ASTRI Project: http://www.brera.inaf.it/~astri/wordpress/
**CTA Project: https://www.cta-observatory.org/

Poster MOPHA032 [1.327 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA032

About •

paper received ※ 02 October 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)