An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models

Alfredo Cuzzocrea, Enzo Mumolo, Marwan Hassani

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    Abstract

    This paper proposes and experimentally assesses a machine learning approach for supporting the effective and efficient generation of synthetic memory reference traces for a wide range of application scenarios. The proposed approach makes a nice use of extended hierarchical Markov models.

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
    PublisherInstitute of Electrical and Electronics Engineers
    Pages2953-2959
    Number of pages7
    ISBN (Electronic)9781538666500
    DOIs
    Publication statusPublished - 16 Jan 2019
    Event2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018) - Miyazaki, Japan
    Duration: 7 Oct 201810 Oct 2018
    http://www.smc2018.org/

    Conference

    Conference2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018)
    Abbreviated titleSMC2018
    CountryJapan
    CityMiyazaki
    Period7/10/1810/10/18
    Internet address

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