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Quantitative Analysis of Apache Storm Applications: The NewsAsset Case Study

Author

Listed:
  • José I. Requeno

    (Universidad de Zaragoza)

  • José Merseguer

    (Universidad de Zaragoza)

  • Simona Bernardi

    (Universidad de Zaragoza)

  • Diego Perez-Palacin

    (Universidad de Zaragoza)

  • Giorgos Giotis

    (Athens Technology Center)

  • Vasilis Papanikolaou

    (Athens Technology Center)

Abstract

The development of Information Systems today faces the era of Big Data. Large volumes of information need to be processed in real-time, for example, for Facebook or Twitter analysis. This paper addresses the redesign of NewsAsset, a commercial product that helps journalists by providing services, which analyzes millions of media items from the social network in real-time. Technologies like Apache Storm can help enormously in this context. We have quantitatively analyzed the new design of NewsAsset to assess whether the introduction of Apache Storm can meet the demanding performance requirements of this media product. Our assessment approach, guided by the Unified Modeling Language (UML), takes advantage, for performance analysis, of the software designs already used for development. In addition, we converted UML into a domain-specific modeling language (DSML) for Apache Storm, thus creating a profile for Storm. Later, we transformed said DSML into an appropriate language for performance evaluation, specifically, stochastic Petri nets. The assessment ended with a successful software design that certainly met the scalability requirements of NewsAsset.

Suggested Citation

  • José I. Requeno & José Merseguer & Simona Bernardi & Diego Perez-Palacin & Giorgos Giotis & Vasilis Papanikolaou, 2019. "Quantitative Analysis of Apache Storm Applications: The NewsAsset Case Study," Information Systems Frontiers, Springer, vol. 21(1), pages 67-85, February.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:1:d:10.1007_s10796-018-9851-x
    DOI: 10.1007/s10796-018-9851-x
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    Cited by:

    1. Justin M. Johnson & Taghi M. Khoshgoftaar, 2020. "The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data," Information Systems Frontiers, Springer, vol. 22(5), pages 1113-1131, October.
    2. Justin M. Johnson & Taghi M. Khoshgoftaar, 0. "The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data," Information Systems Frontiers, Springer, vol. 0, pages 1-19.
    3. Thouraya Bouabana-Tebibel & Stuart H. Rubin & Lydia Bouzar-Benlabiod, 2019. "Guest Editorial: Recent Trends in Reuse and Integration," Information Systems Frontiers, Springer, vol. 21(1), pages 1-3, February.

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