Big-Data Analytics and Cloud Computing

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.

Discusses and explores theoretical concepts, principles, tools, techniques and deployment models in the context of Big Data Focuses on the latest developments in Data Science (aka Analytics) and, especially, their applications to real-world challenges Includes numerous cases studies for in-class analysis and assignments

Autorentext

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

Klappentext
This important and timely text/reference 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
  This authoritative volume will be of great interest to researchers, enterprise architects, business analysts, IT infrastructure managers and application developers, who will benefit from the valuable insights offered into the adoption of architectures for big data and cloud computing. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface.
  The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hillas a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.

  Inhalt

  Part I: Theory.- Data Quality Monitoring of Cloud Databases Based on Data Quality SLAs.- Role and Importance of Semantic Search in Big Data Governance.- Multimedia Big Data: Content Analysis and Retrieval.- An Overview of Some Theoretical Topological Aspects of Big Data.- Part II: Applications.- Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Prediction.- Data Science and Big Data Analytics at CareerBuilder.- Extraction of Bayesian Networks from Large Unstructured Datasets.- Two Case Studies Based on Large Unstructured Sets.- Information Extraction from Unstructured Datasets: An Application to Cardiac Arrhythmia Detection.- A Platform for Analytics on Social Networks Derived from Organizational Calendar Data.