Distributed Computing in Big Data Analytics

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Author : Mamta Mittal
Genre : Computers

Summary : The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

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Author : Neeraj Patel
Genre : Computers

Summary : Big Data Analytics through Mathematical Modeling invites readers to extract meaningful patterns and actionable insights by applying rigorous mathematical techniques to large-scale datasets. This resource bridges foundational mathematics—probability, statistics, linear algebra, optimization—with advanced analytics methodologies for pattern discovery and predictive modeling across industries. Coverage systematically addresses data preprocessing, exploratory analysis techniques, statistical inference methods, and machine learning fundamentals including supervised and unsupervised learning approaches. The book emphasizes practical problem-solving through case studies spanning fraud detection, recommendation systems, social trend analysis, and public policy applications. Real-world examples illustrate analytics applications across North America, Europe, Asia, and emerging economies. Readers develop essential competencies in data manipulation, statistical hypothesis testing, model development, and performance evaluation. Coverage addresses ethical considerations including data privacy, algorithmic fairness, transparency in automated decision-making, and responsible use of predictive models. The text integrates theoretical foundations with computational implementation considerations, ensuring readers understand both conceptual underpinnings and practical execution requirements. This volume serves data scientists, business analysts, researchers, and professionals seeking comprehensive understanding of mathematical approaches to big data analytics and informed decision-making globally.

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Author : Kai Hwang
Genre : Computers

Summary : The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.

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Author : Dr. G. UMADEVI
Genre : Computers

Summary : Abstract. This book provides an in-depth exploration of computational data analytics within the broader context of data science. It covers the fundamental concepts, methodologies, and tools that define the field, while also delving into advanced statistical and machine learning techniques tailored for large datasets. The text is structured to facilitate understanding of both theoretical principles and practical applications, bridging the gap between data analysis and real-world challenges. Ethical considerations, privacy, and data governance are emphasized to ensure readers are aware of the responsibilities that come with handling data. Each chapter is enriched with case studies that illustrate the application of computational data analytics in various domains such as healthcare, finance, and environmental studies. The book concludes with a forward-looking discussion on the future of data analytics, highlighting emerging trends and technologies. Keywords: computational data analytics, data science, machine learning, statistical methods, big data, ethical considerations, real-world applications

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Author : Javier Del Ser
Genre : Computers

Summary : This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.

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Author : Ms.M.Suguna
Genre : Computers

Summary : Ms.M.Suguna, Assistant Professor, Department of Artificial Intelligence and Data Science, Dhanalakshmi Srinivasan University, Samayapuram, Tiruchirappalli, Tamil Nadu, India. Dr.S.Ponnarasi, Assistant Professor, Department of Computer Science, Government Arts and Science College for Women, Veppur, Perambalur, Tamil Nadu, India. Ms.Ramya A, Assistant Professor, Department of Computer Applications, Thanthai Hans Roever College (Autonomous), Perambalur, Tamil Nadu, India. Ms.Lavanya S, Assistant Professor, Department of Computer Applications, Srinivasan College of Arts and Science, Perambalur, Tamil Nadu, India. Dr.P.Kavitha, Assistant Professor, PG & Research Department of Computer Science, Dhanalakshmi Srinivasan College of Arts and Science for Women (Autonomous), Perambalur, Tamil Nadu, India.

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Author : Dr. Rashmita Padhi, Dr. B. Showri Rayalu, Ms. J. Rohini, Dr. D. Radhika
Genre : Computers

Summary : Introduces the fundamentals of data science, covering data processing, analytics, visualization, and practical applications in various domains.