Classification Big Data Analysis and Statistical Learning

Classification   Big  Data Analysis and Statistical Learning
Author: Francesco Mola,Claudio Conversano,Maurizio Vichi
Publsiher: Springer
Total Pages: 242
Release: 2018-02-21
ISBN: 3319557084
Category: Mathematics
Language: EN, FR, DE, ES & NL

Classification Big Data Analysis and Statistical Learning Book Excerpt:

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8–10, 2015.

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
ISBN: 0081006519
Category: Mathematics
Language: EN, FR, DE, ES & NL

Computational and Statistical Methods for Analysing Big Data with Applications Book Excerpt:

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

Big Data Meets Survey Science

Big Data Meets Survey Science
Author: Craig A. Hill,Paul P. Biemer,Trent D. Buskirk,Lilli Japec,Antje Kirchner,Stas Kolenikov,Lars E. Lyberg
Publsiher: John Wiley & Sons
Total Pages: 800
Release: 2020-09-29
ISBN: 1118976320
Category: Social Science
Language: EN, FR, DE, ES & NL

Big Data Meets Survey Science Book Excerpt:

Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering

Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering
Author: Israël César Lerman
Publsiher: Springer
Total Pages: 647
Release: 2016-03-24
ISBN: 1447167937
Category: Computers
Language: EN, FR, DE, ES & NL

Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering Book Excerpt:

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.

Statistical Learning of Complex Data

Statistical Learning of Complex Data
Author: Francesca Greselin,Laura Deldossi,Luca Bagnato,Maurizio Vichi
Publsiher: Springer Nature
Total Pages: 201
Release: 2019-09-06
ISBN: 3030211401
Category: Mathematics
Language: EN, FR, DE, ES & NL

Statistical Learning of Complex Data Book Excerpt:

This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

Statistical Analysis of Microbiome Data

Statistical Analysis of Microbiome Data
Author: Somnath Datta,Subharup Guha
Publsiher: Springer Nature
Total Pages: 346
Release: 2021
ISBN: 3030733513
Category: Big data
Language: EN, FR, DE, ES & NL

Statistical Analysis of Microbiome Data Book Excerpt:

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.

Statistical Learning for Big Dependent Data

Statistical Learning for Big Dependent Data
Author: Daniel Peña,Ruey S. Tsay
Publsiher: John Wiley & Sons
Total Pages: 560
Release: 2021-03-02
ISBN: 1119417392
Category: Mathematics
Language: EN, FR, DE, ES & NL

Statistical Learning for Big Dependent Data Book Excerpt:

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

Big Data Computing

Big Data Computing
Author: Vivek Kale
Publsiher: CRC Press
Total Pages: 495
Release: 2016-11-25
ISBN: 1498715346
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Big Data Computing Book Excerpt:

This book unravels the mystery of Big Data computing and its power to transform business operations. The approach it uses will be helpful to any professional who must present a case for realizing Big Data computing solutions or to those who could be involved in a Big Data computing project. It provides a framework that enables business and technical managers to make optimal decisions necessary for the successful migration to Big Data computing environments and applications within their organizations.

Social Network Based Big Data Analysis and Applications

Social Network Based Big Data Analysis and Applications
Author: Mehmet Kaya,Jalal Kawash,Suheil Khoury,Min-Yuh Day
Publsiher: Springer
Total Pages: 249
Release: 2018-05-10
ISBN: 3319781960
Category: Social Science
Language: EN, FR, DE, ES & NL

Social Network Based Big Data Analysis and Applications Book Excerpt:

This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.

Predictive Analytics Using Statistics and Big Data Concepts and Modeling

Predictive Analytics Using Statistics and Big Data  Concepts and Modeling
Author: Krishna Kumar Mohbey,Arvind Pandey,Dharmendra Singh Rajput
Publsiher: Bentham Science Publishers
Total Pages: 124
Release: 2020-12-09
ISBN: 9811490511
Category: Computers
Language: EN, FR, DE, ES & NL

Predictive Analytics Using Statistics and Big Data Concepts and Modeling Book Excerpt:

This book presents a selection of the latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning and provides studies for readers. The chapters address a comprehensive range of advanced data technologies used for statistical modeling towards predictive analytics. Topics included in this book include: - Categorized machine learning algorithms - Player monopoly in cricket teams. - Chain type estimators - Log type estimators - Bivariate survival data using shared inverse Gaussian frailty models - Weblog analysis - COVID-19 epidemiology This reference book will be of significant benefit to the predictive analytics community as a useful guide of the latest research in this emerging field.

Case Studies in Applied Bayesian Data Science

Case Studies in Applied Bayesian Data Science
Author: Kerrie L. Mengersen,Pierre Pudlo,Christian P. Robert
Publsiher: Springer Nature
Total Pages: 420
Release: 2020-05-28
ISBN: 3030425533
Category: Mathematics
Language: EN, FR, DE, ES & NL

Case Studies in Applied Bayesian Data Science Book Excerpt:

Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration.

Structural Syntactic and Statistical Pattern Recognition

Structural  Syntactic  and Statistical Pattern Recognition
Author: Andrea Torsello,Luca Rossi,Marcello Pelillo,Battista Biggio,Antonio Robles-Kelly
Publsiher: Springer Nature
Total Pages: 378
Release: 2021-04-09
ISBN: 3030739732
Category: Computers
Language: EN, FR, DE, ES & NL

Structural Syntactic and Statistical Pattern Recognition Book Excerpt:

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2020, held in Padua, Italy, in January 2021. The 35 papers presented in this volume were carefully reviewed and selected from 81 submissions. The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding.

Big Data Technologies and Applications

Big Data Technologies and Applications
Author: Borko Furht,Flavio Villanustre
Publsiher: Springer
Total Pages: 400
Release: 2016-09-16
ISBN: 3319445502
Category: Computers
Language: EN, FR, DE, ES & NL

Big Data Technologies and Applications Book Excerpt:

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.

Modern Technologies for Big Data Classification and Clustering

Modern Technologies for Big Data Classification and Clustering
Author: Seetha, Hari,Murty, M. Narasimha,Tripathy, B. K.
Publsiher: IGI Global
Total Pages: 360
Release: 2017-07-12
ISBN: 1522528067
Category: Computers
Language: EN, FR, DE, ES & NL

Modern Technologies for Big Data Classification and Clustering Book Excerpt:

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.

Digital Economy Emerging Technologies and Business Innovation

Digital Economy  Emerging Technologies and Business Innovation
Author: Mohamed Anis Bach Tobji,Rim Jallouli,Ahmed Samet,Mourad Touzani,Vasile Alecsandru Strat,Paul Pocatilu
Publsiher: Springer Nature
Total Pages: 197
Release: 2020-12-02
ISBN: 3030646424
Category: Computers
Language: EN, FR, DE, ES & NL

Digital Economy Emerging Technologies and Business Innovation Book Excerpt:

This book constitutes the refereed proceedings of the 5th International Conference, ICDEc 2020, held in Bucharest, Romania, in June 2020. Due to the COVID-19 pandemic the conference took place virtually. The 13 full papers presented in this volume together with 3 abstracts of keynotes and 1 introductory paper by the steering committee were carefully reviewed and selected from a total of 41 submissions. The core theme of this year’s conference was “Emerging Technologies & Business Innovation”. The papers were organized in four topical sections named: digital transformation, data analytics, digital marketing, and digital business models.

Frontiers in Statistical Quality Control 11

Frontiers in Statistical Quality Control 11
Author: Sven Knoth,Wolfgang Schmid
Publsiher: Springer
Total Pages: 393
Release: 2015-04-24
ISBN: 3319123556
Category: Computers
Language: EN, FR, DE, ES & NL

Frontiers in Statistical Quality Control 11 Book Excerpt:

The main focus of this edited volume is on three major areas of statistical quality control: statistical process control (SPC), acceptance sampling and design of experiments. The majority of the papers deal with statistical process control, while acceptance sampling and design of experiments are also treated to a lesser extent. The book is organized into four thematic parts, with Part I addressing statistical process control. Part II is devoted to acceptance sampling. Part III covers the design of experiments, while Part IV discusses related fields. The twenty-three papers in this volume stem from The 11th International Workshop on Intelligent Statistical Quality Control, which was held in Sydney, Australia from August 20 to August 23, 2013. The event was hosted by Professor Ross Sparks, CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia and was jointly organized by Professors S. Knoth, W. Schmid and Ross Sparks. The papers presented here were carefully selected and reviewed by the scientific program committee, before being revised and adapted for this volume.

Research Anthology on Mental Health Stigma Education and Treatment

Research Anthology on Mental Health Stigma  Education  and Treatment
Author: Management Association, Information Resources
Publsiher: IGI Global
Total Pages: 1305
Release: 2021-02-05
ISBN: 1799885992
Category: Medical
Language: EN, FR, DE, ES & NL

Research Anthology on Mental Health Stigma Education and Treatment Book Excerpt:

In times of uncertainty and crisis, the mental health of individuals become a concern as added stressors and pressures can cause depression, anxiety, and stress. Today, especially with more people than ever experiencing these effects due to the Covid-19 epidemic and all that comes along with it, discourse around mental health has gained heightened urgency. While there have always been stigmas surrounding mental health, the continued display of these biases can add to an already distressing situation for struggling individuals. Despite the experience of mental health issues becoming normalized, it remains important for these issues to be addressed along with adequate education about mental health so that it becomes normalized and discussed in ways that are beneficial for society and those affected. Along with raising awareness of mental health in general, there should be a continued focus on treatment options, methods, and modes for healthcare delivery. The Research Anthology on Mental Health Stigma, Education, and Treatment explores the latest research on the newest advancements in mental health, best practices and new research on treatment, and the need for education and awareness to mitigate the stigma that surrounds discussions on mental health. The chapters will cover new technologies that are impacting delivery modes for treatment, the latest methods and models for treatment options, how education on mental health is delivered and developed, and how mental health is viewed and discussed. It is a comprehensive view of mental health from both a societal and medical standpoint and examines mental health issues in children and adults from all ethnicities and socio-economic backgrounds and in a variety of professions, including healthcare, emergency services, and the military. This book is ideal for psychologists, therapists, psychiatrists, counsellors, religious leaders, mental health support agencies and organizations, medical professionals, teachers, researchers, students, academicians, mental health practitioners, and more.

Seriation in Combinatorial and Statistical Data Analysis

Seriation in Combinatorial and Statistical Data Analysis
Author: Israël César Lerman,Henri Leredde
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN: 303092694X
Category: Combinatorial analysis
Language: EN, FR, DE, ES & NL

Seriation in Combinatorial and Statistical Data Analysis Book Excerpt:

This monograph offers an original broad and very diverse exploration of the seriation domain in data analysis, together with building a specific relation to clustering. Relative to a data table crossing a set of objects and a set of descriptive attributes, the search for orders which correspond respectively to these two sets is formalized mathematically and statistically. State-of-the-art methods are created and compared with classical methods and a thorough understanding of the mutual relationships between these methods is clearly expressed. The authors distinguish two families of methods: Geometric representation methods Algorithmic and Combinatorial methods Original and accurate methods are provided in the framework for both families. Their basis and comparison is made on both theoretical and experimental levels. The experimental analysis is very varied and very comprehensive. Seriation in Combinatorial and Statistical Data Analysis has a unique character in the literature falling within the fields of Data Analysis, Data Mining and Knowledge Discovery. It will be a valuable resource for students and researchers in the latter fields.

Statistical Analysis of Empirical Data

Statistical Analysis of Empirical Data
Author: Scott Pardo
Publsiher: Springer Nature
Total Pages: 277
Release: 2020-05-04
ISBN: 3030433285
Category: Mathematics
Language: EN, FR, DE, ES & NL

Statistical Analysis of Empirical Data Book Excerpt:

Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.

Cognitive Computing for Big Data Systems Over IoT

Cognitive Computing for Big Data Systems Over IoT
Author: Arun Kumar Sangaiah,Arunkumar Thangavelu,Venkatesan Meenakshi Sundaram
Publsiher: Springer
Total Pages: 375
Release: 2017-12-30
ISBN: 3319706888
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Cognitive Computing for Big Data Systems Over IoT Book Excerpt:

This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.