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Full Description
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.
Contents
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.
