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

doi:10.1109/SMC.2018.00502

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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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers
Pages	2953-2959
Number of pages	7
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00502
Publication status	Published - 16 Jan 2019
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018 http://www.smc2018.org/

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC2018
Abbreviated title	SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18
Internet address	http://www.smc2018.org/

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10.1109/SMC.2018.00502

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Cuzzocrea, A., Mumolo, E., & Hassani, M. (2019). An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 2953-2959). Article 8616498 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SMC.2018.00502

Cuzzocrea, Alfredo ; Mumolo, Enzo ; Hassani, Marwan. / An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers, 2019. pp. 2953-2959

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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.",

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Cuzzocrea, A, Mumolo, E & Hassani, M 2019, An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616498, Institute of Electrical and Electronics Engineers, pp. 2953-2959, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00502

An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. / Cuzzocrea, Alfredo; Mumolo, Enzo; Hassani, Marwan.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers, 2019. p. 2953-2959 8616498.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

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Cuzzocrea A, Mumolo E, Hassani M. An effective and efficient approach for supporting the generation of synthetic memory reference traces via hierarchical hidden/non-hidden Markov Models. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers. 2019. p. 2953-2959. 8616498 doi: 10.1109/SMC.2018.00502

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

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