Multidimensional Data Visualization

This book takes an in depth look at a novel methodology for analyzing Global Positioning System (GPS) data to obtain the highest possible resolution surface imaging of tectonic deformation sources wit

Introduction to Statistical Investigations, 1st Edition leads readers to learn about the process of conducting statistical investigations from data collection, to exploring data, to statistical i

To function in modern society complex data must be absorbed and understood at a breakneck pace. The most efficient way to do this is through data-based graphics. This book is an exploration and celebr

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

This book constitutes the refereed proceedings of the 17th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2015, held in Valencia, Spain, September 2015.The 31 revised full

SAS Essentials: A Guide to Mastering SAS, Second Edition features a step-by-step approach to data analysis and interpretation and provides a foundational introduction to SAS statistical software. SAS

This concise set of course-based notes provides the reader with the main concepts and tools to perform statistical analysis of experimental data, in particular in the field of high-energy physics (HEP

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

In the era of "big data," science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such n

Bridging the gap between the marketer who must put text analytics to use and data analysis experts,Practical Text Analytics is an accessible guide to the many advances in text analytics. It explains t

This book constitutes the thoroughly refereed post-conference proceedings of the 19th International Conference on Financial Cryptography and Data Security, FC 2014, held in San Juan, Puerto Rico, in J

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk a

This book constitutes the proceedings of the First International Conference on Big Data Computing and Communications, BigCom 2015, held in Taiyuan, China, in August 2015. The 41 papers presented in th

You’re making progress towards an agile data warehouse and are looking forward to faster results for half the cost. Now it’s time to collaborate with IT managers, project managers, and tech leads acro

This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is heade

"This book cuts through the haze of glitz and pomp surrounding big data and offers a simple, straightforward reference-source of practical academic utility by covering such topics as cloud computing,

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

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

Ralf Karrenberg presents Whole-Function Vectorization (WFV), an approach that allows a compiler to automatically create code that exploits data-parallelism using SIMD instructions. Data-parallel appli

This greatly expanded 2nd edition provides a practical introduction to - data processing with Linux tools and the programming languages AWK and Perl- data management with the relational database syste

This book offers a framework for the analysis of political communication in election campaigns based on digital trace data that documents political behavior, interests and opinions. The author investi

Unique in its integration of theory and application, The Practice of Survey Research explains survey design, implementation, data analysis, and continuing data management, including how to effectively

If you are a data engineer, an application developer, or a data scientist who would like to leverage the power of Apache Spark to get better insights from big data, then this is the book for you.

Social media and the internet are rich, fertile sources of data for social researchers, though online data offer both opportunities and challenges. In this updated new edition, Robert V. Kozinets expl

Social media and the internet are rich, fertile sources of data for social researchers, though online data offer both opportunities and challenges. In this updated new edition, Robert V. Kozinets expl

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

People are an organizations' biggest asset and easily amount to 30% of company costs so even small improvements can have a bottom-line impact.People Data demystifies and simplifies the process of unde

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to s

These notes describe how to average and fit numerical data that have been obtained either by simulation or measurement. Following an introduction on how to estimate various average values, they discus

How can Twitter data be used to study individual-level human behavior and social interaction on a global scale? This book introduces readers to the methods, opportunities, and challenges of using Twitter data to analyze phenomena ranging from the number of people infected by the flu, to national elections, to tomorrow's stock prices. Each chapter, written by leading domain experts in clear and accessible language, takes the reader to the forefront of the newly emerging field of computational social science. An introductory chapter on Twitter data analysis provides an overview of key tools and skills, and gives pointers on how to get started, while the case studies demonstrate shortcomings, limitations, and pitfalls of Twitter data as well as its advantages. The book will be an excellent resource for social science students and researchers wanting to explore the use of online data.

How can Twitter data be used to study individual-level human behavior and social interaction on a global scale? This book introduces readers to the methods, opportunities, and challenges of using Twitter data to analyze phenomena ranging from the number of people infected by the flu, to national elections, to tomorrow's stock prices. Each chapter, written by leading domain experts in clear and accessible language, takes the reader to the forefront of the newly emerging field of computational social science. An introductory chapter on Twitter data analysis provides an overview of key tools and skills, and gives pointers on how to get started, while the case studies demonstrate shortcomings, limitations, and pitfalls of Twitter data as well as its advantages. The book will be an excellent resource for social science students and researchers wanting to explore the use of online data.

Drawing on data from a Europe wide project, together with existing data on equality and diversity initiatives, this book explores the work of trade unions in supporting equality and anti-discriminatio

While millions of consumers carry on unaware, powerful companies are racing to gain more knowledge and data than anyone, including any government, has ever had. The goal is to understand consumer beha

An Oracle ACE Director reveals how to design world-class databases on any platform with this powerful, free tool.Oracle SQL Developer Data Modeler for Database Design Mastery provides best practices f

"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

"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

Object-Role Modeling (ORM) is a fact-based approach to data modeling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All

Two recent innovations, the emergence of formal cognitive models and the addition of cognitive neuroscience data to the traditional behavioral data, have resulted in the birth of a new, interdisciplin

Quantification methodology of categorical data is a popular topic in many branches of science. Most books, however, are either too advanced for those who need it, or too elementary to gain insight int

Subtitled: My Journey into the Secret World of U.S. Nuclear Weapons Design Data. Thoroughly covers the functionality, secrecy, and history of U.S. nuclear weapons, ballistic missiles, and reentry vehi