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

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 ebook experiences the theoretical techniques, modern ideas and sensible instruments thinking about the newest multi-disciplinary ways addressing the demanding situations of huge facts. Illuminating views from either academia and are awarded via a global choice of specialists in large info technology. themes and contours: describes the cutting edge advances in theoretical elements of huge info, predictive analytics and cloud-based architectures; examines the functions and implementations that make the most of giant information in cloud architectures; surveys the state-of-the-art in architectural techniques to the supply of cloud-based mammoth information analytics capabilities; identifies power study instructions and applied sciences to facilitate the belief of rising company versions via significant info techniques; offers correct theoretical frameworks, empirical study findings, and diverse case stories; discusses real-world functions of algorithms and strategies to handle the demanding situations of huge datasets.

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Extra resources for Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications

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Even though the role of the CDO still needs to be defined, we can roughly say that the CDO understands data and their value in the context of the organization’s purpose. Furthermore, this understanding is a common one, collectively shared among all information consumers within an organization. The CDO, thus, can take the role of the data steward and can help to manage the collective design of the semantic layer and its vocabularies. There are many discovery tasks that serve individual, ad hoc, and transient purposes.

Information consumers can usually sketch their information demand that summarizes the data they need to solve their information problem. They have a deep understanding of the foundations of their domain. Thus, we need a stronger involvement of humans in the value chain of Big Data analysis. Sustainable success in Big Data, however, requires more than just controlling the results produced by Big Data analytics. The integration of more competence, in particular domain competence, means a more active role of the human actor in all stages of the value chain.

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.

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