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Fingerprinting and Reconstruction of Functionals of Discrete Time Markov Chains

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Analytical and Stochastic Modelling Techniques and Applications (ASMTA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9845))

Abstract

We explore various fingerprinting options for functionals of Markov chains with a low number of parameters describing both stationary behaviour and correlation over time. We also present reconstruction methods using lazy Markov chains for the various options. The proposed methods allow for efficient simulation of input data with statistical properties similar to actual real data to serve as realistic input of a data processing system. Possible applications include resource allocation in data processing systems. The methods are validated on data from real-life telecommunication systems.

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Author information

Authors and Affiliations

  1. Department of Differential Equations, Budapest University of Technology and Economics, Budapest, Hungary

    Attila Egri

  2. MTA-BME Information Systems Research Group, Budapest, Hungary

    Illés Horváth

  3. Nokia, Bell-Labs, Murray Hill, USA

    Ferenc Kovács

  4. Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary

    Roland Molontay

Authors
  1. Attila Egri
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  2. Illés Horváth
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  3. Ferenc Kovács
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  4. Roland Molontay
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Corresponding author

Correspondence to Illés Horváth .

Editor information

Editors and Affiliations

  1. Ghent University , Gent, Belgium

    Sabine Wittevrongel

  2. University of Tsukuba , Tsukuba, Japan

    Tuan Phung-Duc

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Egri, A., Horváth, I., Kovács, F., Molontay, R. (2016). Fingerprinting and Reconstruction of Functionals of Discrete Time Markov Chains. In: Wittevrongel, S., Phung-Duc, T. (eds) Analytical and Stochastic Modelling Techniques and Applications. ASMTA 2016. Lecture Notes in Computer Science(), vol 9845. Springer, Cham. https://doi.org/10.1007/978-3-319-43904-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-43904-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43903-7

  • Online ISBN: 978-3-319-43904-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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