A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

Alfredo Cuzzocrea; Enzo Mumolo; Marwan Hassani; Giorgio Mario Grasso

doi:10.18293/DMSVIVA2018-018

A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

Alfredo Cuzzocrea, Enzo Mumolo, Marwan Hassani, Giorgio Mario Grasso

Process Science

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

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Abstract

Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition in computer science research. The goal is that of reproducing the running of an arbitrary program, whose generated traces can later be used for simulations and experiments. In this paper we investigate this research context and provide principles and algorithms of a Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Specifically, our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/non Hidden Markov Model (HHnHMM). Experimental results conclude this paper..

Original language	English
Title of host publication	Proceedings - DMSVIVA 2018
Subtitle of host publication	Proceedings of the 24th International DMS Conference on Visualization and Visual Languages
Place of Publication	Pittsburgh
Publisher	Knowledge Systems Institute Graduate School
Pages	83-90
Number of pages	8
ISBN (Electronic)	1891706454, 9781891706455
DOIs	https://doi.org/10.18293/DMSVIVA2018-018
Publication status	Published - 1 Jan 2018
Event	24th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2018 - Redwood City, United States Duration: 29 Jun 2018 → 30 Jun 2018

Conference

Conference	24th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2018
Country/Territory	United States
City	Redwood City
Period	29/06/18 → 30/06/18

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10.18293/DMSVIVA2018-018

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Cuzzocrea, A., Mumolo, E., Hassani, M., & Grasso, G. M. (2018). A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. In Proceedings - DMSVIVA 2018: Proceedings of the 24th International DMS Conference on Visualization and Visual Languages (pp. 83-90). Knowledge Systems Institute Graduate School. https://doi.org/10.18293/DMSVIVA2018-018

Cuzzocrea, Alfredo ; Mumolo, Enzo ; Hassani, Marwan et al. / A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Proceedings - DMSVIVA 2018: Proceedings of the 24th International DMS Conference on Visualization and Visual Languages. Pittsburgh : Knowledge Systems Institute Graduate School, 2018. pp. 83-90

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title = "A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently",

abstract = "Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition in computer science research. The goal is that of reproducing the running of an arbitrary program, whose generated traces can later be used for simulations and experiments. In this paper we investigate this research context and provide principles and algorithms of a Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Specifically, our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/non Hidden Markov Model (HHnHMM). Experimental results conclude this paper..",

author = "Alfredo Cuzzocrea and Enzo Mumolo and Marwan Hassani and Grasso, {Giorgio Mario}",

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doi = "10.18293/DMSVIVA2018-018",

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Cuzzocrea, A, Mumolo, E, Hassani, M & Grasso, GM 2018, A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. in Proceedings - DMSVIVA 2018: Proceedings of the 24th International DMS Conference on Visualization and Visual Languages. Knowledge Systems Institute Graduate School, Pittsburgh, pp. 83-90, 24th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2018, Redwood City, United States, 29/06/18. https://doi.org/10.18293/DMSVIVA2018-018

A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. / Cuzzocrea, Alfredo; Mumolo, Enzo; Hassani, Marwan et al.
Proceedings - DMSVIVA 2018: Proceedings of the 24th International DMS Conference on Visualization and Visual Languages. Pittsburgh: Knowledge Systems Institute Graduate School, 2018. p. 83-90.

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

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AB - Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition in computer science research. The goal is that of reproducing the running of an arbitrary program, whose generated traces can later be used for simulations and experiments. In this paper we investigate this research context and provide principles and algorithms of a Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Specifically, our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/non Hidden Markov Model (HHnHMM). Experimental results conclude this paper..

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Cuzzocrea A, Mumolo E, Hassani M, Grasso GM. A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. In Proceedings - DMSVIVA 2018: Proceedings of the 24th International DMS Conference on Visualization and Visual Languages. Pittsburgh: Knowledge Systems Institute Graduate School. 2018. p. 83-90 doi: 10.18293/DMSVIVA2018-018

A Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

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