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.
Trovati, M., Hill, R., Anjum, A., Zhu, S. Y., & Liu, L. (2016). Big-data analytics and cloud computing: Theory, algorithms and applications. Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications (pp. i–xvi). Springer International Publishing. https://doi.org/10.1007/978-3-319-25313-8