Big-Data Analytics and Cloud Computing

Zusatztext

<p>This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.</p>

Autorenportrait

<p>The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where <b>Dr. Marcello Trovati</b> serves as a Senior Lecturer in Mathematics, <b>Dr. Richard Hill</b> as a Professor and Head of the Computing and Mathematics Department, <b>Dr. Ashiq Anjum</b> as a Professor of Distributed Computing, <b>Dr. Shao Ying Zhu</b> as a Senior Lecturer in Computing, and <b>Dr. Lu Liu</b> as a Professor of Distributed Computing. The other publications of the editors include the Springer titles <i>Guide to Security Assurance for Cloud Computing</i>, <i>Guide to Cloud Computing</i> and <i>Cloud Computing for Enterprise Architectures</i>.</p>

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