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Big Data Semantics

Paolo Ceravolo, Antonia Azzini, Marco Angelini, Tiziana Catarci, Philippe Cudré-Mauroux, Ernesto Damiani, Alexandra Mazak, Maurice Van Keulen, Mustafa Jarrar, Giuseppe Santucci, Kai-Uwe Sattler, Monica Scannapieco, Manuel Wimmer, Robert Wrembel, Fadi Zaraket: Big Data Semantics. In: Journal on Data Semantics, 2018, ISSN: 1861-2040.

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Abstract

Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.

BibTeX (Download)

@article{Ceravolo2018bds,
title = {Big Data Semantics},
author = {Paolo Ceravolo and Antonia Azzini and Marco Angelini and Tiziana Catarci and Philippe Cudré-Mauroux and Ernesto Damiani and Alexandra Mazak and Maurice Van Keulen and Mustafa Jarrar and Giuseppe Santucci and Kai-Uwe Sattler and Monica Scannapieco and Manuel Wimmer and Robert Wrembel and Fadi Zaraket},
doi = {10.1007/s13740-018-0086-2},
issn = {1861-2040},
year  = {2018},
date = {2018-05-23},
journal = {Journal on Data Semantics},
abstract = {Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.},
keywords = {Big Data},
pubstate = {published},
tppubtype = {article}
}