This book constitutes the proceedings of the 9th International Conference on Big Data Analytics, BDA 2021, which took place virtually during December 7–9, 2021. The 15 full papers and 1 short paper included in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections as follows: Data science: systems; data science: architectures; big data analytics in healthcare support systems, information interchange of web data resources; and business analytics.

Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world's largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. - Bridges the gap between radio astronomy and computer science - Includes coverage of the observation lifecycle as well as data collection, processing and analysis - Presents state-of-the-art research and techniques in big data related to radio astronomy - Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)

This book constitutes the proceedings of the 10th International Conference on Big Data Analytics, BDA 2022, which took place in a hybrid mode during December 2022 in Aizu, Japan. The 14 full papers included in this volume were carefully reviewed and selected from 70 submissions. They were organized in topical sections as follows: big data analytics, networking, social media, search, information extraction, image processing and analysis, spatial, text, mobile and graph data analysis, machine learning, and healthcare.

Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. In-depth discussion of data analysis for scientists and engineers Coverage of both frequentist and Bayesian approaches to data analysis Extensive look at analysis techniques for time-series data and images Detailed exploration of linear and nonlinear modeling of data Emphasis on error analysis Instructor's manual (available only to professors)

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. - Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies - Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics - Unique case study approach provides readers with insights for practical clinical implementation

"Big Data Analytics is a field that dissects, efficiently extricates data from, or in any case manages informational indexes that are excessively huge or complex to be managed by customary information preparing application programming. Information with numerous cases (lines) offers more noteworthy factual force, while information with higher multifaceted nature may prompt a higher bogus disclosure rate. Enormous information challenges incorporate catching information, information stockpiling, information investigation, search, sharing, move, representation, and questioning, refreshing, data security and data source. Large information was initially connected with three key ideas: volume, variety and velocity. Consequently, huge information regularly incorporates information with sizes that surpass the limit of conventional programming to measure inside a satisfactory time and worth. Current utilization of the term enormous information will in general allude to the utilization of predictive analytics, user behavior analytics, or certain other progressed information investigation techniques that concentrate an incentive from information, and sometimes to a specific size of informational index. There is little uncertainty that the amounts of information now accessible are undoubtedly enormous, however that is not the most important quality of this new information biological system. Investigation of informational indexes can discover new relationships to spot business patterns or models. Researchers, business persons, clinical specialists, promoting and governments consistently meet challenges with huge informational collections in territories including Internet look, fintech, metropolitan informatics, and business informatics. Researchers experience constraints in e-Science work, including meteorology, genomics, connectomics, complex material science reproductions, science and ecological exploration. The main objective of this book is to write about issues, challenges, opportunities, and solutions in novel research projects about big data in various domains. The topics of interest include, but are not limited to: efficient storage, management and sharing large scale of data; novel approaches for analyzing data using big data technologies; implementation of high performance and/or scalable and/or real-time computation algorithms for analyzing big data; usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data; using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in different domains (with large scale datasets); Legal and ethical issues and solutions for using, sharing and publishing large datasets; and the results of data analytics, security and privacy issues"--