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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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.

大数据英语-数据可视化

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

大屏数据可视化综合使用条形图、柱状图、折线图、饼图、地图、数字翻牌器来实现一个常规的大屏数据可视化项目。项目案例 - 上市公司地域分布以百度地图为底图，结合ECharts绘制地理信息数据旭日图 - 基于ECharts V4.2 旭日图（Sunburst）是ECharts 4.0新增的图表类型，由多层的环形图组成，在数据结构上，内圈是外圈的父节点。因此，它既能像饼图一样表现局部和整体的占比，又能像矩形树图一样表现层级关系。树图 - 基于ECharts V4.2 树图是一种流行的利用包含关系表达层次化数据的可视化方法。地图数据可视化 - 基于ECharts Geo 地图热点、飞线动效，世界地图、中国地图、省份地图、城市地图、区县地图 3D图表展示 - 基于ECharts GL 3D柱形图，3D地球，二维数据的3D化展示热力图展示 - 基于ECharts & 百度地图基于百度地图的热力图，基于笛卡尔坐标系的热力图 ECharts扩展示例词云，水球图，烟花秀，关系图谱，中国地图阿里云DataV案例 - 智慧文旅驾驶舱基于阿里云DataV大

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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