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Predictive Analytics: A Shortcut to Dependable Computing

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Software Engineering for Resilient Systems (SERENE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10479))

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The paper lists three major issues: complexity, time and uncertainty, and identifies dependability as the permanent challenge. In order to enhance dependability, the paradigm shift is proposed where focus is on failure prediction and early malware detection. Failure prediction methodology, including modeling and failure mitigation, is presented and two case studies (failure prediction for computer servers and early malware detection) are described in detail. The proposed approach, using predictive analytics, may increase system availability by an order of magnitude or so.

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  1. 1.

    Inspired by a quote from Johann Gottfried von Herder (1744-1803): “Die zwei größten Tyrannen der Erde: der Zufall und die Zeit” (Two biggest tyrants on Earth are: the chance and the time).

  2. 2.

    A system element offering a predefined service and able to communicate with other components.

  3. 3.

    Classes are used to group related features and functions.


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I would like to acknowledge valuable contributions of my students Günther Hoffmann, Igor Kaitovic and Felix Salfner to the methodology and the case study on failure prediction. Alberto Ferrante and Jelena Milosevic contributed to the malware detection methodology and experiments.

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Correspondence to Miroslaw Malek .

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Malek, M. (2017). Predictive Analytics: A Shortcut to Dependable Computing. In: Romanovsky, A., Troubitsyna, E. (eds) Software Engineering for Resilient Systems. SERENE 2017. Lecture Notes in Computer Science(), vol 10479. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65947-3

  • Online ISBN: 978-3-319-65948-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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