Download Big-Data Analytics and Cloud Computing: Theory, Algorithms by Marcello Trovati, Visit Amazon's Richard Hill Page, search PDF

By Marcello Trovati, Visit Amazon's Richard Hill Page, search results, Learn about Author Central, Richard Hill, , Ashiq Anjum, Shao Ying Zhu, Lu Liu

This e-book stories the theoretical thoughts, modern ideas and sensible instruments excited about the most recent multi-disciplinary methods addressing the demanding situations of huge info. Illuminating views from either academia and are awarded by means of a world number of specialists in gigantic information technological know-how. subject matters and lines: describes the cutting edge advances in theoretical elements of huge information, predictive analytics and cloud-based architectures; examines the purposes and implementations that make the most of significant information in cloud architectures; surveys the cutting-edge in architectural methods to the availability of cloud-based tremendous facts analytics features; identifies strength study instructions and applied sciences to facilitate the belief of rising company types via mammoth info methods; presents proper theoretical frameworks, empirical learn findings, and various case experiences; discusses real-world purposes of algorithms and methods to deal with the demanding situations of massive datasets.

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In: Proceedings of the 24th IEEE international conference on advanced information networking and applications, pp 27–33 16. Nascimento DC, Pires CE, Mestre D (2015) A data quality-aware cloud service based on metaheuristic and machine learning provisioning algorithms. In: Proceedings of the 30th ACM/SIGAPP symposium on applied computing, pp 1696–1703 17. Dan A, Davis D, Kearney R, Keller A, King R, Kuebler D, Youssef A (2004) Web services on demand: WSLA-driven automated management. IBM Syst J 43(1):136–158 18.

Springer, pp 349–358. 1007/978-0-387-78446-5_23 24. Reynolds MB, Hopkinson KM, Oxley ME, Mullins BE (2011) Provisioning norm: an asymmetric quality measure for SaaS resource allocation. In: Proceedings of the IEEE international conference on services computing, pp 112–119 25. Kolb L, Thor A, Rahm E (2013) Load balancing for mapreduce-based entity resolution. In: Proceedings of the IEEE 28th international conference on data engineering, pp 618–629 26. Mestre DG, Pires CE (2013) Improving load balancing for mapreduce-based entity matching.

The related literature discusses many different approaches [23] for a penalty model. Thereby, these approaches need to be investigated and evaluated in order to conclude which penalty model is more suitable for the DQaS context. Alternatively, one can also propose and evaluate novel penalty models that are designed specifically for a DQaS. Cost Model As stated earlier in this book chapter, one of the main goals of a service provider in the cloud computing context is to minimize the service costs (and, in turn, customers’ bill) by meeting customers’ requirements (predefined QoS) using the minimum amount of hardware infrastructure.

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