Cristin-resultat-ID: 1980819
Sist endret: 14. mars 2022, 23:36
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview

Bidragsytere:
  • Dumitru Roman
  • Nikolay Nikolov
  • Ahmet Soylu
  • Brian Elvesæter
  • Hui Song
  • Radu Prodan
  • mfl.

Tidsskrift

Proceedings of the IEEE Symposium on Computers and Communications
ISSN 1530-1346
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Publisert online: 2021
Open Access

Beskrivelse Beskrivelse

Tittel

Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview

Sammendrag

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.

Bidragsytere

Dumitru Roman

  • Tilknyttet:
    Forfatter
    ved Sustainable Communication Technologies ved SINTEF AS

Nikolay Nikolov

  • Tilknyttet:
    Forfatter
    ved Sustainable Communication Technologies ved SINTEF AS

Ahmet Soylu

  • Tilknyttet:
    Forfatter
    ved Institutt for informasjonsteknologi ved OsloMet - storbyuniversitetet

Brian Elvesæter

  • Tilknyttet:
    Forfatter
    ved Sustainable Communication Technologies ved SINTEF AS

Hui Song

  • Tilknyttet:
    Forfatter
    ved Sustainable Communication Technologies ved SINTEF AS
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