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Real-time Linked Dataspaces pp 297–304Cite as

Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems

Abstract

As we move toward 2030, today’s computing paradigms such as data-intensive computing (Big Data), Open Data [380], Knowledge Graphs, Machine Learning, Large-Scale Distributed Systems [381], Internet of Things (IoT), Physical-Cyber-Social Computing [14], Service-Oriented [382], and Cloud/Edge Computing [383] will be the foundations to the realisation of the vision of intelligent systems. In fact, real-world intelligent systems are being enabled by a combination of these paradigms using a mixture of architectures (centralised, decentralised, and a combination of both) and infrastructures such as Middleware and IoT platforms to support the development of intelligent systems and applications [13, 67, 295, 384].

Keywords

  • Dataspaces
  • Data ecosystems
  • Intelligent systems
  • Research challenges
  • Technology adoption
  • Trusted data sharing
  • Governance
  • Incremental systems engineering
  • Human-centricity

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  1. National University of Ireland Galway, Galway, Ireland

    Edward Curry

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  1. Edward Curry
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Correspondence to Edward Curry .

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Curry, E. (2020). Future Research Directions for Dataspaces, Data Ecosystems, and Intelligent Systems. In: Real-time Linked Dataspaces. Springer, Cham. https://doi.org/10.1007/978-3-030-29665-0_18

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  • DOI: https://doi.org/10.1007/978-3-030-29665-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29664-3

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