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
Country/TerritoryJapan
CityMiyazaki
Period7/10/1810/10/18
Internet address

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