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 publication stories the theoretical options, modern innovations and useful instruments keen on the newest multi-disciplinary methods addressing the demanding situations of massive info. Illuminating views from either academia and are offered by means of a global choice of specialists in enormous information technology. themes and contours: describes the leading edge advances in theoretical facets of huge information, predictive analytics and cloud-based architectures; examines the functions and implementations that make the most of vast information in cloud architectures; surveys the state-of-the-art in architectural techniques to the availability of cloud-based significant facts analytics capabilities; identifies capability learn instructions and applied sciences to facilitate the conclusion of rising company types via gigantic info ways; presents suitable theoretical frameworks, empirical learn findings, and diverse case stories; discusses real-world purposes of algorithms and strategies to deal with the demanding situations of huge datasets.
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Additional info for Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications
Overall the focus has been on empirical studies rather than the theoretical so as to highlight the most practically successful recent developments and highlight the associated caveats or lessons learned. 1 Introduction Multimedia data has been called the ‘biggest big data’ . There is an ongoing explosion in the volume and ubiquity of multimedia data. It approximately makes up 60 % of Internet traffic and 70 % of mobile phone traffic and 70 % of all available unstructured data . Although in a general person’s day-to-day life it may appear that multimedia data belongs to the realm of the social media, in fact there are many other sources for multimedia data.
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.
Nascimento et al. analysis. In this sense, data warehousing techniques may be used in order to model a multidimensional database that can be used to store subject-oriented, temporal, and nonvolatile data generated by the execution of data quality algorithms over time. Penalty Model In the DQaS context, it is necessary to adopt a penalty model to calculate the amount of penalty that is inflicted to the DQaS for the cases in which the service does not meet the time restriction, specified in the DQSLA, for the execution of a data quality algorithm.
Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications by Marcello Trovati, Visit Amazon's Richard Hill Page, search results, Learn about Author Central, Richard Hill, , Ashiq Anjum, Shao Ying Zhu, Lu Liu