Big Data Semantics
Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing.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 coordinate...
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Veröffentlicht in: | Journal on data semantics 2018-06, Vol.7 (2), p.65-85 |
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container_title | Journal on data semantics |
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creator | Ceravolo, Paolo Azzini, Antonia Angelini, Marco Catarci, Tiziana Cudré-Mauroux, Philippe Damiani, Ernesto Mazak, Alexandra Van Keulen, Maurice Jarrar, Mustafa Santucci, Giuseppe Sattler, Kai-Uwe Scannapieco, Monica Wimmer, Manuel Wrembel, Robert Zaraket, Fadi |
description | Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing.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. |
doi_str_mv | 10.1007/s13740-018-0086-2 |
format | Article |
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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. 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subjects | Analytics Artificial Intelligence Big Data Computer Science Data management Data processing Database Management Information Storage and Retrieval Information Systems Applications (incl.Internet) IT in Business Literature reviews Original Article Semantics |
title | Big Data Semantics |
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