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Enhancing Big Data Application Design with the DICE Framework

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Advances in Service-Oriented and Cloud Computing (ESOCC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 824))

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Abstract

The focus of the DICE project is to define a quality-driven framework for developing Big data applications. DICE offers an Eclipse-based development environment, centered around a novel UML profile, to prototype, deploy, monitor, and test Big data applications. The DICE framework has been designed to natively support popular open-source solutions. The framework offers a set of 15 open source tools, which have been validated against industrial case studies in the news and media, port operations, and e-government domains.

C. Li—This paper has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 644869. Project full name: DICE - Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements: Feb 2015–2018, website: www.dice-h2020.eu.

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Notes

  1. 1.

    LINE website: http://line-solver.sf.net.

References

  1. Casale, G., et al.: DICE: quality-driven development of data-intensive cloud applications. In: Proceedings of MiSE Workshop (2015)

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  2. Li, C., Altamimi, T., Zargari, M.H., Casale, G., Petriu, D.: Tulsa: a tool for transforming UML to layered queueing networks for performance analysis of data intensive applications. In: Bertrand, N., Bortolussi, L. (eds.) QEST 2017. LNCS, vol. 10503, pp. 295–299. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66335-7_18

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  3. Li, C., Casale, G.: Performance-aware refactoring of cloud-based big data applications. In: Proceedings of CSCI-ISCC (2017)

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  4. Spinner, S., Casale, G., Brosig, F., Kounev, S.: Evaluating approaches to resource demand estimation. Perform. Eval. 92, 51–71 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Imperial College London, London, UK

    Giuliano Casale & Chen Li

Authors
  1. Giuliano Casale
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  2. Chen Li
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Corresponding author

Correspondence to Chen Li .

Editor information

Editors and Affiliations

  1. University of Duisburg-Essen, Essen, Germany

    Zoltán Ádám Mann

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Volker Stolz

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Casale, G., Li, C. (2018). Enhancing Big Data Application Design with the DICE Framework. In: Mann, Z., Stolz, V. (eds) Advances in Service-Oriented and Cloud Computing. ESOCC 2017. Communications in Computer and Information Science, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-319-79090-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-79090-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-79089-3

  • Online ISBN: 978-3-319-79090-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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