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Efficient Service Selection in Multimedia Documents Adaptation Processes

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Book cover Pattern Recognition and Artificial Intelligence (MedPRAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1322))

Abstract

Pervasive systems help access to multimedia documents at any time, from anywhere and through several devices (smart TV, laptop, tablet, etc.). Nevertheless, due to changes in users’ contexts (e.g. noisy environment, preferred language, public place, etc.), restrictions on correct access to these documents may be imposed. One possible solution is to adapt their contents using adaptation services so that they comply, as far as possible, with the current constraints. In this respect, several adaptation approaches have been proposed. However, when it comes to selecting the required adaptation services, they often carry out this task according to predefined configurations or deterministic algorithms. Actually, the efficient selection of adaptation services is one of the key-elements involved in improving the quality of service in adaptation processes. To deal with this issue (i.e. the efficient selection of adaptation services), we first provide an enriched problem formulation as well as methods that we use in problem-solving. Then, we involve standard and compact evolutionary algorithms to find efficient adaptation plans. The standard version is usually adopted in systems that are not subject to specific constraints. The compact one is used in systems for which constraints on computational resources and execution time are considered. The proposal is validated through simulation, experiments and comparisons according to performance, execution time and energy consumption. The obtained results are satisfactory and encouraging.

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Acknowledgments

We would like to thank the Direction Generale de la Recherche Scientifique et du Developpement Technologique (DGRSDT) in Algeria, for supporting this research work. Also, the authors would like to thank Dr. Amer Draa from MISC Laboratory - University of Constantine 2, Algeria, for the feedback and discussions on optimization concerns and issues.

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Authors and Affiliations

  1. RELA(CS)2 Laboratory, University of Oum El-Bouaghi, Route de Constantine BP 321, 04000, Oum El-Bouaghi, DZ, Algeria

    Zakaria Laboudi, Asma Saighi & Hamana Nazim Hamri

  2. MISC Laboratory, University of Constantine, 2, Nouvelle ville Ali Mendjeli BP 67A, 25100, Constantine, DZ, Algeria

    Abdelkader Moudjari

Authors
  1. Zakaria Laboudi
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  2. Abdelkader Moudjari
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  3. Asma Saighi
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  4. Hamana Nazim Hamri
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Corresponding author

Correspondence to Zakaria Laboudi .

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Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Digital Research Center of Sfax, Sfax, Tunisia

    Yousri Kessentini

  3. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  4. Digital Research Centre of Sfax, Sfax, Tunisia

    Mohamed Jmaiel

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Laboudi, Z., Moudjari, A., Saighi, A., Hamri, H.N. (2021). Efficient Service Selection in Multimedia Documents Adaptation Processes. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-71804-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71803-9

  • Online ISBN: 978-3-030-71804-6

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