This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.

Professor Zaigham Mahmood is a Senior Technology Consultant at Debesis Education UK and Associate Lecturer (Research) at the University of Derby, UK. He also holds positions as Foreign Professor at NUST and IIU in Islamabad, Pakistan, and Professor Extraordinaire at the North West University Potchefstroom, South Africa. Prof. Mahmood is a certified cloud computing instructor and a regular speaker at international conferences devoted to Cloud Computing and E-Government. His specialized areas of research include distributed computing, project management, and e-government. Among his many publications are the Springer titles Cloud Computing: Challenges, Limitations and R&D Solutions, Continued Rise of the Cloud, Cloud Computing: Methods and Practical Approaches, Software Engineering Frameworks for the Cloud Computing Paradigm, and Cloud Computing for Enterprise Architectures.

Part I: Data Science Applications and Scenarios.- An Interoperability Framework and Distributed Platform for Fast Data Applications.- Complex Event Processing Framework for Big Data Applications.- Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios.- Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective.- Part II: Big Data Modelling and Frameworks.- A Unified Approach to Data Modelling and Management in Big Data Era.- Interfacing Physical and Cyber Worlds: A Big Data Perspective.- Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data.- An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data Repositories.- Part III: Big Data Tools and Analytics.- Large Scale Data Analytics Tools: Apache Hive, Pig and HBase.- Big Data Analytics: Enabling Technologies and Tools.- A Framework for Data Mining and Knowledge Discovery in Cloud Computing.- Feature Selection for Adaptive Decision Making in Big Data Analytics.- Social Impact and Social Media Analysis Relating to Big Data.