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Authors: Luigi Sgaglione 1 ; Gaetano Papale 1 ; Giovanni Mazzeo 1 ; Gianfranco Cerullo 1 ; Pasquale Starace 1 and Ferdinando Campanile 2

Affiliations: 1 University of Naples "Parthenope", Italy ; 2 Sync Lab S.r.l., Italy

Keyword(s): Big Data, Data Collection.

Abstract: The data collection for eventual analysis is an old concept that today receives a revisited interest due to the emerging of new research trend such Big Data. Furthermore, considering that a current market trend is to provide integrated solution to achieve multiple purposes (such as ISOC, SIEM, CEP, etc.), the data became very heterogeneous. In this paper a flexible and efficient solution about the data collection of heterogeneous data is presented, describing the approach used to collect heterogeneous data and the additional features (pre-processing) provided with it.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Sgaglione, L.; Papale, G.; Mazzeo, G.; Cerullo, G.; Starace, P. and Campanile, F. (2016). Data Collection Framework - A Flexible and Efficient Tool for Heterogeneous Data Acquisition. In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1: DataDiversityConvergence; ISBN 978-989-758-182-3; ISSN 2184-5042, SciTePress, pages 374-379. DOI: 10.5220/0005931103740379

@conference{datadiversityconvergence16,
author={Luigi Sgaglione. and Gaetano Papale. and Giovanni Mazzeo. and Gianfranco Cerullo. and Pasquale Starace. and Ferdinando Campanile.},
title={Data Collection Framework - A Flexible and Efficient Tool for Heterogeneous Data Acquisition},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1: DataDiversityConvergence},
year={2016},
pages={374-379},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005931103740379},
isbn={978-989-758-182-3},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1: DataDiversityConvergence
TI - Data Collection Framework - A Flexible and Efficient Tool for Heterogeneous Data Acquisition
SN - 978-989-758-182-3
IS - 2184-5042
AU - Sgaglione, L.
AU - Papale, G.
AU - Mazzeo, G.
AU - Cerullo, G.
AU - Starace, P.
AU - Campanile, F.
PY - 2016
SP - 374
EP - 379
DO - 10.5220/0005931103740379
PB - SciTePress