Download Cloud Computing: 5th International Conference, CloudComp by Victor C.M. Leung, Roy Xiaorong Lai, Min Chen, Jiafu Wan PDF

By Victor C.M. Leung, Roy Xiaorong Lai, Min Chen, Jiafu Wan

This booklet constitutes the completely refereed publish convention complaints of the fifth overseas convention on Cloud Computing, CloudComp 2014, held in Guilin, China, in October 2014. The 25 revised complete papers have been rigorously reviewed and chosen from seventy two submissions and canopy themes equivalent to cellular cloud computing, providers, purposes, IoT on cloud, architectures and massive info, cloud-assisted pervasive computing and companies, administration and virtualization for cloud, cloud security.

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Extra info for Cloud Computing: 5th International Conference, CloudComp 2014, Guilin, China, October 19-21, 2014, Revised Selected Papers

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Facilitating bioinformatic research with mobile cloud. In: The Second International Conference on Cloud Computing, GRIDs, and Virtualization, CLOUD COMPUTING 2011 (2011) 9. : A cloud collaboration system with active application control scheme and its experimental performance analysis, Master thesis, Korea Advanced Institute of Science and Technology (2012) 10. : More for your money: exploiting performance heterogeneity in public clouds. In: Proceedings of the Third ACM Symposium on Cloud Computing.

Finally, theoretical analysis and extensive results are supplemented to show the advantages of the proposed system. Keywords: Recommender system 1 · Revisiting ratio · Reference model Introduction When the original conceptual foundation of the Internet was laid back in the 50s, no one had the slightest idea of how easy it will be for us to receive information. With the easy access to the Internet, users can acquire the most recent knowledge and information, including daily news, friend’s moments, new movies, almost everywhere with their laptop, iPad, smartphones, and other forms of smart devices.

User2’s profile in the same time. This user only watches videos on some days, but the amount he/she watches is a lot each time. Different from user 1, he/she watches roughly the same amount of new items as the old ones. are eventually more than user 1. Therefore, when the system makes personalized recommendations, it should clearly consider both the frequency and amount of the watching to decide how many recommendations it should make every day to different users. Finally, we examine at the composition of the daily user profiles.

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