Multidimensional Data Visualization

What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gather

For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: N

The Complete Book of Data Anonymization: From Planning to Implementation supplies a 360-degree view of data privacy protection using data anonymization. It examines data anonymization from both a prac

Bringing a practitioner’s view to big data analytics, this work examines the drivers behind big data, postulates a set of use cases, identifies sets of solution components, and recommends various impl

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts.

Data Insights offers multi-disciplinary perspectives and useful information about how visualizations can open your eyes to data. This thought-provoking book takes a conversational approach to presenti

Unique insights to implement big data analytics and reap big returns to your bottom lineFocusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohl

Boost your Big Data IQ! Gain insight into how to govern and consume IBM's unique in-motion and at-rest Big Data analytic capabilitiesBig Data represents a new era of computing'an inflection point of o

The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioin

You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring D

In his latest book, bestselling author Gregory Michie critiques high-stakes schooling and provides a powerful alternative vision of teaching as a humanistic enterprise, students as multidimensional be

Each edition of Introduction to Data Compression has widely been considered the best introduction and reference text on the art and science of data compression, and the?fourth edition continues in thi

Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the pract

This book covers important aspects of fundamental research in data provenance and data management(DPDM), including provenance representation and querying, as well as practical applications in such dom

Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time

As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis software. Addressing performance issues, Cloud Computing: Data

Are you struggling with the formal design of your organization's data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping th

Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become sel

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mini

Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same e

For Business Data Communications, Data Communications, and introductory Networking for Business courses. Business Data Communications: Infrastructure, Networking and Security covers the fundamenta

Congestion Control in Data Transmission Networks details the modeling and control of data traffic in communication networks. It shows how various networking phenomena can be represented in a consisten

Statisticians and researchers will find Categorical Data Analysis Using SAS, Third Edition, by Maura Stokes, Charles Davis, and Gary Koch, to be a useful discussion of categorical data analysis techni

This book is for any manager or team leader that has the green light to implement a data governance program. The problem of managing data continues to grow with issues surrounding cost of storage, exp

Increasingly, environmental decision making is like playing a multidimensional game of chess. With interactions between the atmosphere, the litho-hydrosphere, and the biosphere, the game is at once a

Data virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems.

Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from

Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practi

How do you approach answering queries when your data is stored in multiple databases that were designed independently by different people? This is first comprehensive book on data integration and is w

One of a series of pocket-sized books that provide a quick reference to development data on different topics, 'The Little Data Book on Private Sector Development 2012' provides data for more than 20 k

Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about im

Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categori

Food nanotechnology is an expanding field. This expansion is based on the advent of new technologies for nanostructure characterization, visualization, and construction. Nanotechnology Research Method

Data assimilation is a novel, versatile methodology for estimating atmospheric and oceanic variables. The estimation of a quantity of interest via data assimilation involves the combination of observa

This lively, comprehensive introduction to human behavior in the social environment offers a multidimensional approach to the topic, with discussion of integrative practice, theory, treatment, and ser

The spread of the immigration points system worldwide shows that policymaking is multidimensional and interdependent. This book examines states' pursuit of global talent and the globalization of the "

This comprehensive volume presents the foundations of linear algebra ideas and techniques applied to data mining and related fields. Linear algebra has gained increasing importance in data mining and

The Little Green Data Book is a pocket-sized ready reference on key environmental data for over 200 countries. Key indicators are organized under the headings of agriculture, forestry, biodiversity, o

Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecti

Data replication by employing quorum systems is an important concept to improve operation availability on data objects in distributed systems that have strong data consistency demands. These data repl