Computational and Statistical Methods for Analysing Big Data with Applications

Computational and Statistical Methods for Analysing Big Data with Applications
Author: Shen Liu,James Mcgree,Zongyuan Ge,Yang Xie
Publsiher: Academic Press
Total Pages: 206
Release: 2015-11-20
Genre: Mathematics
ISBN: 9780081006511

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Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate

Data Analytics Computational Statistics and Operations Research for Engineers

Data Analytics  Computational Statistics  and Operations Research for Engineers
Author: Debabrata Samanta,SK Hafizul Islam,Naveen Chilamkurti,Mohammad Hammoudeh
Publsiher: CRC Press
Total Pages: 296
Release: 2022-04-05
Genre: Technology & Engineering
ISBN: 9781000550429

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With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

Data Analysis and Applications 3

Data Analysis and Applications 3
Author: Andreas Makrides,Alex Karagrigoriou,Christos H. Skiadas
Publsiher: John Wiley & Sons
Total Pages: 262
Release: 2020-05-05
Genre: Business & Economics
ISBN: 9781786305343

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Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.

Handbook of Big Data Analytics

Handbook of Big Data Analytics
Author: Wolfgang Karl Härdle,Henry Horng-Shing Lu,Xiaotong Shen
Publsiher: Springer
Total Pages: 538
Release: 2018-07-20
Genre: Computers
ISBN: 9783319182841

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Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

Applications in Statistical Computing

Applications in Statistical Computing
Author: Nadja Bauer,Katja Ickstadt,Karsten Lübke,Gero Szepannek,Heike Trautmann,Maurizio Vichi
Publsiher: Springer Nature
Total Pages: 340
Release: 2019-10-12
Genre: Computers
ISBN: 9783030251475

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This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.

Computational Methods for Data Analysis

Computational Methods for Data Analysis
Author: Yeliz Karaca,Carlo Cattani
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 395
Release: 2018-12-17
Genre: Mathematics
ISBN: 9783110496369

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This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

Cyber Defense Mechanisms

Cyber Defense Mechanisms
Author: Gautam Kumar,Dinesh Kumar Saini,Nguyen Ha Huy Cuong
Publsiher: CRC Press
Total Pages: 216
Release: 2020-09-20
Genre: Computers
ISBN: 9781000171921

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This book discusses the evolution of security and privacy issues and brings related technological tools, techniques, and solutions into one single source. The book will take readers on a journey to understanding the security issues and possible solutions involving various threats, attacks, and defense mechanisms, which include IoT, cloud computing, Big Data, lightweight cryptography for blockchain, and data-intensive techniques, and how it can be applied to various applications for general and specific use. Graduate and postgraduate students, researchers, and those working in this industry will find this book easy to understand and use for security applications and privacy issues.

Functional Statistics and Related Fields

Functional Statistics and Related Fields
Author: Germán Aneiros,Enea G. Bongiorno,Ricardo Cao,Philippe Vieu
Publsiher: Springer
Total Pages: 288
Release: 2017-04-25
Genre: Mathematics
ISBN: 9783319558462

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This volume collects latest methodological and applied contributions on functional, high-dimensional and other complex data, related statistical models and tools as well as on operator-based statistics. It contains selected and refereed contributions presented at the Fourth International Workshop on Functional and Operatorial Statistics (IWFOS 2017) held in A Coruña, Spain, from 15 to 17 June 2017. The series of IWFOS workshops was initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008. Since then, many of the major advances in functional statistics and related fields have been periodically presented and discussed at the IWFOS workshops.

Research Anthology on Big Data Analytics Architectures and Applications

Research Anthology on Big Data Analytics  Architectures  and Applications
Author: Management Association, Information Resources
Publsiher: IGI Global
Total Pages: 1988
Release: 2021-09-24
Genre: Computers
ISBN: 9781668436639

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Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians.

Modern Statistical Methods for Health Research

Modern Statistical Methods for Health Research
Author: Yichuan Zhao,(Din) Ding-Geng Chen
Publsiher: Springer
Total Pages: 392
Release: 2021-10-21
Genre: Medical
ISBN: 3030724360

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This book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include: Health data analysis and applications to EHR data Clinical trials, FDR, and applications in health science Big network analytics and its applications in GWAS Survival analysis and functional data analysis Graphical modelling in genomic studies The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.

Big Data and Social Science

Big Data and Social Science
Author: Ian Foster,Rayid Ghani,Ron S. Jarmin,Frauke Kreuter,Julia Lane
Publsiher: CRC Press
Total Pages: 391
Release: 2020-11-18
Genre: Mathematics
ISBN: 9781000208634

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Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available data and code as well as practical programming exercises through Binder and GitHub New to the Second Edition Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Frontiers in Massive Data Analysis

Frontiers in Massive Data Analysis
Author: National Research Council,Division on Engineering and Physical Sciences,Board on Mathematical Sciences and Their Applications,Committee on Applied and Theoretical Statistics,Committee on the Analysis of Massive Data
Publsiher: National Academies Press
Total Pages: 190
Release: 2013-09-03
Genre: Mathematics
ISBN: 9780309287814

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Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.

The Behavioral and Social Sciences

The Behavioral and Social Sciences
Author: National Research Council,Division of Behavioral and Social Sciences and Education,Commission on Behavioral and Social Sciences and Education,Committee on Basic Research in the Behavioral and Social Sciences
Publsiher: National Academies Press
Total Pages: 304
Release: 1988-02-01
Genre: Science
ISBN: 9780309037495

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This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research.

Statistical Analysis of Microbiome Data

Statistical Analysis of Microbiome Data
Author: Somnath Datta,Subharup Guha
Publsiher: Springer Nature
Total Pages: 346
Release: 2021
Genre: Big data
ISBN: 9783030733513

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Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.

Open Source Software for Statistical Analysis of Big Data Emerging Research and Opportunities

Open Source Software for Statistical Analysis of Big Data  Emerging Research and Opportunities
Author: Segall, Richard S.,Niu, Gao
Publsiher: IGI Global
Total Pages: 237
Release: 2020-02-21
Genre: Computers
ISBN: 9781799827702

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With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.