Big Data Visualization下载

PIPI_333 2018-08-16 09:06:36
The target audience of this book are data analysts and those with at least a basic knowledge of big data analysis who now want to learn interesting approaches to big data visualization in order to make their analysis more valuable. Readers who possess adequate knowledge of big data platform tools such as Hadoop or have exposure to programming languages such as R can use this book to learn additional approaches (using various technologies) for addressing the inherent challenges of visualizing big data.
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About This Book, Get acquainted with a set of fundamental OpenGL primitives and concepts that enable users to create stunning visuals of arbitrarily complex 2D and 3D datasets for many common applicationsExplore interactive, real-time visualization of large 2D and 3D datasets or models, including the use of more advanced techniques such as stereoscopic 3D rendering.Create stunning visuals on the latest platforms including mobile phones and state-of-the-art wearable computing devices, Who This Book Is For, This book is aimed at anyone interested in creating impressive data visualization tools using modern graphics hardware. Whether you are a developer, engineer, or scientist, if you are interested in exploring the power of OpenGL for data visualization, this book is for you. While familiarity with C/C++ is recommended, no previous experience with OpenGL is assumed., What You Will Learn, Install, compile, and integrate the OpenGL pipeline into your own projectCreate interactive applications using GLFW and handle user inputs with callback functionsUse OpenGL primitives and features in the OpenGL Shading Language (GLSL)Render complex 3D volumetric data with techniques such as data slicers and multiple viewpoint projectionImplement a hardware-accelerated data visualizer, heat map generator, point cloud rendering, perspective rendering, and alpha blendingProcess images or video sources with texture mapping and custom fragment shader programs for image resizing and wrappingDevelop video see-through augmented reality applications with OpenGLVisualize 3D models using meshes and surfaces with dynamic lighting, In Detail, OpenGL is a great multi-platform, cross-language, and hardware-accelerated graphics interface for visualizing large 2D and 3D datasets. Data visualization has become increasingly challenging using conventional approaches as datasets become larger and larger, especially with the Big Data evolution. From a mobile device to a sophisticated high-performance compu
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. Table of Contents Chapter 1 Introduction to Big Data Chapter 2 Big Data Analytics Chapter 3 Transfer Learning Techniques Chapter 4 Visualizing Big Data Chapter 5 Deep Learning Techniques in Big Data Analytics Chapter LexisNexis Risk Solution to Big Data Chapter 6 The HPCC/ECL Platform for Big Data Chapter 7 Scalable Automated Linking Technology for Big Data Computing Chapter 8 Aggregated Data Analysis in HPCC Systems Chapter 9 Models for Big Data Chapter 10 Data Intensive Supercomputing Solutions Chapter 11 Graph Processing with Massive Datasets: A Kel Primer Chapter Big Data Applications Chapter 12 HPCC Systems for Cyber Security Analytics Chapter 13 Social Network Analytics: Hidden and Complex Fraud Schemes Chapter 14 Modeling Ebola Spread and Using HPCC/KEL System Chapter 15 Unsupervised Learning and Image Classification in High Performance Computing Cluster

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