Java Data Object

本書全面系統地講述了如何利用Java語言解決實際問題，重點剖析了數據結構和數據抽象的核心概念，并通過大量示例向讀者展示了面向對象程序設計理念的精髓。本書在第1版的基礎上完善了所有的Java代碼，使用UML處理了所有偽代碼，通過準確的概念講解、貼切的示例和范圍廣泛的問題討論，使老師和學生的教與學都變得輕鬆自如。本書能夠使讀者系統地掌握問題求解技術和相關的編程技能，為日后的軟件開發工作打下堅實的基礎。

本書是作者根據最新計算機教學大綱，并總結多年從事Java語言程序設計的教學經驗編寫而成的。 本書全面講解了Java的基礎內容和編程方法，在內容的深度和廣度方面都給予了認真的考慮，在類、對象、繼承、接口等重要的基礎知識上側重深度，而在實用類的講解上側重廣度。另外，還以具體的案例介紹了本書知識的綜合應用；而且每章都配有一定數量的習題或思考題，便于讀者復習參考。通過學習，讀者可以掌握Java面向對象編程

The new edition of this acclaimed book gives a fully updated overview of European data protection law affecting companies, incorporating the important legal developments which have taken place since t

There is a growing interest in developing register-based surveys; that is surveys based upon already available administrative data. Since huge amounts of such data are generated within various adminis

本書全面、翔實地介紹了基于優秀的Java開發平臺——Eclipse，進行插件、界面和Web開發方面的內容。書中不僅包括適合Java初學者閱讀的編譯和運行Java應用程序，在Eclipse中配置環境變量、構建路徑，使用Junit編寫測試代碼等Eclipse開發的基礎知識；還包括適合Eclipse具體項目開發人員閱讀的開發Java圖形應用程序，開發Web應用程序，為Eclipse平臺開發插件等Ecli

本書根據教育部考試中心2004年最新發布的《全國計算機等級考試大綱》編寫，針對計算機等級考試二級Java程序設計各方面的考點進行講解和訓練。本書前11章概括了二級Java程序設計筆試方面的知識，第12章是上機指導部分。各章的主要內容有：知識點、重點、難點（列出考試的核心知識點）；典型試題及解析（筆試題的各類題型的分析以及精要解答）；自我訓練題和答案（大量的練習題以及答案）。最后提供了兩套模擬試卷，

本書介紹Java EE與.NET集成策略和最佳實踐。涉及計算機系統集成過程中遇到的各種問題，例如多樣的技術集、不兼容的API和完全不同的環境維護等。主要內容包括兩個方面：首先是集成類型方面的內容，例如同步和異步，概述了獨立的Java和.NET組件是如何運用多種多樣的技術集成在一起的；其次是與服務需求質量有關的內容，例如安全性、可靠性、可伸縮性，以及滿足不同企業環境的服務水平協議的要素。 本書作者

While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and preventi

This volume presents an extensive collection of contributions covering aspects of the exciting and important research field of data mining techniques in biomedicine. Coverage includes new approaches f

“Just do an Internet search.” “It’s on the Internet” These phrases have quickly become a part of the vernacular. The quintessential book of data relating to water, The Water Encyclopedia: Hydrologic D

Drawing upon her experience in a data-driven school system, Blink describes how such practice interacts with a culture of continuous improvement, the needs of individual students, an environment of hi

Shows ordinary users how to tap the rich data analysis functionality of Excel, make sense of their organization's critical financial and statistical information, and put together compelling data prese

本書通過理論與實踐代碼相結合的方式，力求用簡單、直接的方式使讀者學會使用Java語言進行編程。 全書共分27章，內容涵蓋了Java編程環境概述、基礎語法、面向對象軟件設計方法、線程、數據集合、網絡編程、圖形編程、多媒體編程以及Java Web開發。本書每一節的例子都是精挑細選的，具有很經聽針對性，力求讓讀者通過實踐掌握Java編程的基本方法 本書適合沒有編程經驗的初、中級讀者，高等院校計算機相

Information Generation is the story of the seminal role data plays in our lives and explains how the advance of our civilization has come hand in hand with our ability to collect and interpret data. S

本書是關于Spring 2.0的權威教程，是Java/Java EE開發者必備的參考書。本書詳盡系統地介紹了Java EE的基礎知識、Spring 2.0的各種功能，以及Spring 2.0的高級使用技巧和最佳實踐。全書共分為4篇：第1篇介紹Spring 2.0核心技術，主要圍繞Spring元框架進行闡述；第2篇介紹DAO層集成技術，主要圍繞JDBC、Hibernate和JPA等持久化技術展開論述

As one of the fastest growing technologies in our culture today, data communications and networking presents a unique challenge for instructors. As both the number and types of students are increasing

本書對面向對象的思想和機制進行了準確而透徹的剖析，為讀者深入學習Java語言程序設計提供了全面、詳細的指導。全書覆蓋了面向對象設計的廣泛內容，介紹了Java語言的基本數據類型、流程控制、類和對象等。在深入分析面向對象設計方法的基礎上，介紹了封裝機制，繼承機制和多態性的實現與應用，異常的捕捉和處理，以及集合類的定義方法和泛型類的概念。書中提供了大量具有可實踐性的程序實例、自測題及答案、練習題和編程項

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concept

Businesses now rely almost entirely on applications and databases, causing data and storage needs to increase at astounding rates. It is therefore imperative for a company to optimize and simplify the

This book is intended to provide a guide to the universe of data content produced by the global capital markets on a daily basis. Commonly referred to as “market data?, the universe of content is ver

There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and sh

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal backgro

With this book Paul Virilio inaugurated the new science whose object of study is the "dromocratic" revolution.

Fathom Dynamic Data Software Version 2 is an easy-to-use software that bridges the gap between concrete and abstract mathematical and statistical concepts. This software lets students see and manipul

Social scientists have long relied on a wide range of tools to collect information about the social world, but as individual fields have become more specialised, researchers are trained to use a narrow range of the possible data collection methods. This book, first published in 2006, draws on a broad range of available social data collection methods to formulate a set of data collection approaches. The approaches described here are ideal for social science researchers who plan to collect new data about people, organisations, or social processes. Axinn and Pearce present methods designed to create a comprehensive empirical description of the subject being studied, with an emphasis on accumulating the information needed to understand what causes what with a minimum of error. In addition to providing methodological motivation and underlying principles, the book is filled with detailed instructions and concrete examples for those who wish to apply the methods to their research.

Pro OGRE 3D Programming offers a detailed guide to the cross-platform Object-Oriented Graphics Rendering Engine (OGRE) 3D engine. OGRE provides an object-oriented interface to render 3D scenes. Common

Social scientists have long relied on a wide range of tools to collect information about the social world, but as individual fields have become more specialised, researchers are trained to use a narrow range of the possible data collection methods. This book, first published in 2006, draws on a broad range of available social data collection methods to formulate a set of data collection approaches. The approaches described here are ideal for social science researchers who plan to collect new data about people, organisations, or social processes. Axinn and Pearce present methods designed to create a comprehensive empirical description of the subject being studied, with an emphasis on accumulating the information needed to understand what causes what with a minimum of error. In addition to providing methodological motivation and underlying principles, the book is filled with detailed instructions and concrete examples for those who wish to apply the methods to their research.

It is now possible to predict the future when it comes to crime. In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforceme

In this text, students of applied mathematics, science and engineering are introduced to fundamental ways of thinking about the broad context of parallelism. The authors begin by giving the reader a deeper understanding of the issues through a general examination of timing, data dependencies, and communication. These ideas are implemented with respect to shared memory, parallel and vector processing, and distributed memory cluster computing. Threads, OpenMP, and MPI are covered, along with code examples in Fortran, C, and Java. The principles of parallel computation are applied throughout as the authors cover traditional topics in a first course in scientific computing. Building on the fundamentals of floating point representation and numerical error, a thorough treatment of numerical linear algebra and eigenvector/eigenvalue problems is provided. By studying how these algorithms parallelize, the reader is able to explore parallelism inherent in other computations, such as Monte Carlo

In this text, students of applied mathematics, science and engineering are introduced to fundamental ways of thinking about the broad context of parallelism. The authors begin by giving the reader a deeper understanding of the issues through a general examination of timing, data dependencies, and communication. These ideas are implemented with respect to shared memory, parallel and vector processing, and distributed memory cluster computing. Threads, OpenMP, and MPI are covered, along with code examples in Fortran, C, and Java. The principles of parallel computation are applied throughout as the authors cover traditional topics in a first course in scientific computing. Building on the fundamentals of floating point representation and numerical error, a thorough treatment of numerical linear algebra and eigenvector/eigenvalue problems is provided. By studying how these algorithms parallelize, the reader is able to explore parallelism inherent in other computations, such as Monte Carlo

Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes

With accompanying software! Clinicians manage a lot of data - on assorted bits of paper and in their heads. This book is about better ways to manage and understand large amounts of clinical data. Fol

This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic and c

Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This

A visually intuitive approach to statistical data analysis Visual Statistics brings the most complex and advanced statistical methods within reach of those with little statistical training by

Furnishing essential data on all areas of toxicity testing, this Second Edition provides guidance on the design and evaluation of product safety studies to help ensure regulatory acceptance.? Every ch

This book helps you make sense of the data your school district collects, including state student achievement results as well as other qualitative and quantitative data. Easy-to-use templates, tools,

In recent years, companies and government agencies have come to realize that the data they use represent a significant corporate resource, whose cost calls for management every bit as rigorous as the