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Big Data Pipelines on the Computing Continuum: Tapping the Dark Data

Roman, Dumitru; Prodan, Radu; Nikolov, Nikolay; Soylu, Ahmet; Matskin, Mihhail; Marrella, Andrea; Kimovski, Dragi; Elvesæter, Brian; Simonet-Boulogne, Anthony; Ledakis, Giannis; Song, Hui; Leotta, Francesco; Kharlamov, Evgeny
Peer reviewed, Journal article
Accepted version
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URI
https://hdl.handle.net/11250/3048498
Date
2022-10-25
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  • Publikasjoner fra Cristin [4188]
  • TKD - Institutt for informasjonsteknologi [1041]
Original version
Computer. 2022, 55 (11), 74-84.   https://doi.org/10.1109/MC.2022.3154148
Abstract
Big Data pipelines are essential for leveraging Dark Data, i.e., data collected but not used and turned into value. However, tapping their potential requires going beyond existing approaches and frameworks for Big Data processing. The Computing Continuum enables new opportunities for managing Big Data pipelines concerning efficient management of heterogeneous and untrustworthy resources. This article discusses the Big Data pipelines lifecycle on the Computing Continuum, its associated challenges and outlines a future research agenda in this area.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Series
Computer;Volume: 55, Issue: 11
Journal
Computer

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