Java Data Object

?This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: b

This book presents new approaches that advance research in all aspects of agent-based models, technologies, simulations and implementations for data intensive applications. The nine chapters contain a

Türk, a freelance business consultant in Germany, offers a guide to each step of the data archiving process in the SAP (Systems Applications and Products) system. He outlines the basic principles and

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built

This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a uni

There has been a data rush in the past decade brought about by online communication and, in particular, social media (Facebook, Twitter, Youtube, among others), which promises a new age of digital enl

Amid the proliferating cross-disciplinary discussion of ‘big data’ and algorithms, there is currently a lack of academic work on the changing landscape of calculative technologies and devices, this vo

This book introduces the field of Health Web Science and presents methods for information gathering from written social media data. It explores the availability and utility of the personal medical inf

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and func

This complete how-to guidebook provides you with the tools and inspiration you need to use infographics and data visualization techniques in your library to knock your audience’s socks off as you tell

This volume contains nineteen research papers belonging to theareas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their

Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge tech

Fluoroplastics, Volume 2: Melt Processible Fluoropolymers - The Definitive User's Guide and Data Bookcompiles the working knowledge of the polymer chemistry and physics of melt processible fluoropolym

Technological advancements in computing have changed how data is leveraged by businesses to develop, grow, and innovate. In recent years, leading analytical companies have begun to realize the value i

This book details how to leverage big data style analytics to manage and coordinate the key issues in both energy supply and demand. It presents a detailed explanation of the underlying systems techno

At last, a book that puts ad creative inits proper place to drive brand success.Ad-itude empowers and advises advertisersand agencies to leverage the data to unleashcreative power, impact brand perfor

This book presents how multimedia data analysis, information retrieval and indexing are central for comprehensive, personalized, adaptive quality care and the prolongation of independent living at hom

This book contains a selection of papers from the 16th International Symposium on Spatial Data Handling (SDH), the premier long-running forum in geographical information science. This collection offer

The exponential growth of structured and unstructured social data has confronted fields such as political science, sociology, psychology, information systems, public health, public policy, and communi

In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for

This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s stru

At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overvi

This book provides a step-by-step guide to designing and implementing an enterprise data warehouse using a 4-iteration lifecycle and standardized design pattern that enables fast implementation of a f

The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancemen

This collection of research papers provides extensive information on deploying services, concepts, and approaches for using open linked data from libraries and other cultural heritage institutions. Wi

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a

This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the clas

"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are i

This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and

Doing Sociolinguistics: A practical guide to data collection and analysis provides an accessible introduction and guide to the methods of data collection and analysis in the field of sociolinguistics.

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement

This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The w

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent yearsThis book covers kernel analysis topics ranging from the fundamental theo

This handbook represents a milestone in the progression of Data Envelopment Analysis (DEA). Written by experts who are often major contributors to DEA theory, it includes a collection of chapters that

This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics

This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the inf

Utilizing unique real-world examples and numerous graphics, this introductory book provides a solid foundation of the statistical methods that are used to analyze biomarker data and features software

This book provides an introduction into data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. In addition, this revi

This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book an

“I didn’t set out to become a collector of your and your neighbors’ information. When I was growing up, nobody but egghead scientists talked about ‘data.’ It was the mechanical age, and I was a gadget