This book is a printed edition of the Special Issue "Scalable Interactive Visualization" that was published in Informatics

Create and publish your own interactive data visualization projects on the Web, even if you have no experience with either web development or data visualization. It’s easy with this hands-on guide. You’ll start with an overview of data visualization concepts and simple web technologies, and then learn how to use D3, a JavaScript library that lets you express data as visual elements in a web page. Interactive Data Visualization for the Web makes these skills available at an introductory level for designers and visual artists without programming experience, journalists interested in the emerging data journalism processes, and others keenly interested in visualization and publicly available data sources. Get a practical introduction to data visualization, accessible for beginners Focus on web-based tools that help you publish your creations quickly to a wide audience Learn about interactivity so you can engage users in exploring your data

A comprehensive discussion of the findings of the PICASSO initiative on ICT policy ICT Policy, Research, and Innovation: Perspectives and Prospects for EU-US Collaboration provides a clearly readable overview of selected information and communication technology (ICT) and policy topics. Rather than deluge the reader with technical details, the distinguished authors provide just enough technical background to make sense of the underlying policy discussions. The book covers policy, research, and innovation topics on technologies as wide-ranging as: Internet of Things Cyber physical systems 5G Big data ICT Policy, Research, and Innovation compares and contrasts the policy approaches taken by the EU and the US in a variety of areas. The potential for future cooperation is outlined as well. Later chapters provide policy perspectives about some major issues affecting EU/US development cooperation, while the book closes with a discussion of how the development of these new technologies is changing our conceptions of fundamental aspects of society.

Encyclopedia of Computer Graphics and Games (ECGG) is a unique reference resource tailored to meet the needs of research and applications for industry professionals and academic communities worldwide. The ECGG covers the history, technologies, and trends of computer graphics and games. Editor Newton Lee, Institute for Education, Research, and Scholarships, Los Angeles, CA, USA Academic Co-Chairs Shlomo Dubnov, Department of Music and Computer Science and Engineering, University of California San Diego, San Diego, CA, USA Patrick C. K. Hung, University of Ontario Institute of Technology, Oshawa, ON, Canada Jaci Lee Lederman, Vincennes University, Vincennes, IN, USA Industry Co-Chairs Shuichi Kurabayashi, Cygames, Inc. & Keio University, Kanagawa, Japan Xiaomao Wu, Gritworld GmbH, Frankfurt am Main, Hessen, Germany Editorial Board Members Leigh Achterbosch, School of Science, Engineering, IT and Physical Sciences, Federation University Australia Mt Helen, Ballarat, VIC, Australia Ramazan S. Aygun, Department of Computer Science, Kennesaw State University, Marietta, GA, USA Barbaros Bostan, BUG Game Lab, Bahçeşehir University (BAU), Istanbul, Turkey Anthony L. Brooks, Aalborg University, Aalborg, Denmark Guven Catak, BUG Game Lab, Bahçeşehir University (BAU), Istanbul, Turkey Alvin Kok Chuen Chan, Cambridge Corporate University, Lucerne, Switzerland Anirban Chowdhury, Department of User Experience and Interaction Design, School of Design (SoD), University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India Saverio Debernardis, Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Bari, Italy Abdennour El Rhalibi, Liverpool John Moores University, Liverpool, UK Stefano Ferretti, Department of Computer Science and Engineering, University of Bologna, Bologna, Italy Han Hu, School of Information and Electronics, Beijing Institute of Technology, Beijing, China Ms. Susan Johnston, Select Services Films Inc., Los Angeles, CA, USA Chris Joslin, Carleton University, Ottawa, Canada Sicilia Ferreira Judice, Department of Computer Science, University of Calgary, Calgary, Canada Hoshang Kolivand, Department Computer Science, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK Dario Maggiorini, Department of Computer Science, University of Milan, Milan, Italy Tim McGraw, Purdue University, West Lafayette, IN, USA George Papagiannakis, ORamaVR S.A., Heraklion, Greece; FORTH-ICS, Heraklion Greece University of Crete, Heraklion, Greece Florian Richoux, Nantes Atlantic Computer Science Laboratory (LINA), Université de Nantes, Nantes, France Andrea Sanna, Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy Yann Savoye, Institut fur Informatik, Innsbruck University, Innsbruck, Austria Sercan Şengün, Wonsook Kim School of Art, Illinois State University, Normal, IL, USA Ruck Thawonmas, Ritsumeikan University, Shiga, Japan Vinesh Thiruchelvam, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia Rojin Vishkaie, Amazon, Seattle, WA, USA Duncan A. H. Williams, Digital Creativity Labs, Department of Computer Science, University of York, York, UK Sai-Keung Wong, National Chiao Tung University, Hsinchu, Taiwan Editorial Board Intern Sam Romershausen, Vincennes University, Vincennes, IN, USA

Data Warehousing and Data Mining is presented in a question-and-answer format following the examination pattern and covers all key topics in the syllabus. The book is designed to make learning fast and effective and is precise, up-to-date and will help students excel in their examinations. The book is part of the Express Learning is a series of books designed as quick reference guides to important undergraduate courses. The organized and accessible format of these books allows students to learn important concepts in an easy-to-understand, question-and-answer format. These portable learning tools have been designed as one-stop references for students to understand and master the subjects by themselves.

This volume, in conjunction with the two volumes CICS 0002 and LNAI 4682, constitutes the refereed proceedings of the Third International Conference on Intelligent Computing held in Qingdao, China, in August 2007. The 139 full papers published here were carefully reviewed and selected from among 2,875 submissions. Collectively, these papers represent some of the most important findings and insights into the field of intelligent computing.

Due to the increasing need to solve complex problems, high-performance computing (HPC) is now one of the most fundamental infrastructures for scientific development in all disciplines, and it has progressed massively in recent years as a result. HPC facilitates the processing of big data, but the tremendous research challenges faced in recent years include: the scalability of computing performance for high velocity, high variety and high volume big data; deep learning with massive-scale datasets; big data programming paradigms on multi-core; GPU and hybrid distributed environments; and unstructured data processing with high-performance computing. This book presents 19 selected papers from the TopHPC2017 congress on Advances in High-Performance Computing and Big Data Analytics in the Exascale era, held in Tehran, Iran, in April 2017. The book is divided into 3 sections: State of the Art and Future Scenarios, Big Data Challenges, and HPC Challenges, and will be of interest to all those whose work involves the processing of Big Data and the use of HPC.

Transforming data into actionable insights using Python KEY FEATURES ● Gain a comprehensive understanding of data visualization and exploratory data analysis (EDA) using Python. ● Discover valuable insights and patterns in data through visual analysis. ● Master the art of effectively communicating complex concepts by creating compelling and impactful data visualizations. DESCRIPTION Python is a popular programming language for data visualization due to its rich ecosystem of libraries and tools. If you're interested in delving into data visualization in Python, this book is an excellent resource to begin your journey. With Matplotlib, you'll master the art of creating a wide range of charts, plots, and graphs. From basic line plots to complex 3D visualizations, you'll learn how to transform raw data into engaging visuals that tell compelling stories. Dive into Seaborn, a high-level library built on top of Matplotlib, and discover how to effortlessly create beautiful and informative statistical visualizations effortlessly. From heatmaps to distribution plots, you'll unleash the full potential of Seaborn in your data analysis endeavors. Lastly, you will learn how to unleash the true potential of Bokeh and create compelling data visualizations that allow users to explore and interact with data dynamically. By the end of the book, you will have acquired the knowledge and skills necessary to create a diverse range of visualizations proficiently. WHAT YOU WILL LEARN ● Utilize Matplotlib, Seaborn, and Bokeh to produce visually captivating visualizations. ● Gain expertise in various types of charts, plots, and graphs. ● Craft visually appealing and informative statistical visualizations. ● Construct interactive and adaptable plots using Bokeh. ● Explore various techniques for conducting Exploratory Data Analysis (EDA). WHO THIS BOOK IS FOR This book caters to a wide audience, including undergraduate and postgraduate students, researchers, data managers, and data analysts. It presents an all-encompassing exploration of data visualization, equipping you with the essential groundwork to progress as a data-driven professional. TABLE OF CONTENTS 1. Understanding Data 2. Data Visualization – Importance 3. Data Visualization Use Cases 4. Data Visualization Tools and Techniques 5. Data Visualization with Matplotlib 6. Data Visualization with Seaborn 7. Data Visualization with Bokeh 8. Exploratory Data Analysis

The growth in the Bioinformatics and Computational Biology fields over the last few years has been remarkable and the trend is to increase its pace. In fact, the need for computational techniques that can efficiently handle the huge amounts of data produced by the new experimental techniques in Biology is still increasing driven by new advances in Next Generation Sequencing, several types of the so called omics data and image acquisition, just to name a few. The analysis of the datasets that produces and its integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Within this scenario of increasing data availability, Systems Biology has also been emerging as an alternative to the reductionist view that dominated biological research in the last decades. Indeed, Biology is more and more a science of information requiring tools from the computational sciences. In the last few years, we have seen the surge of a new generation of interdisciplinary scientists that have a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. PACBB‘11 hopes to contribute to this effort promoting this fruitful interaction. PACBB'11 technical program included 50 papers from a submission pool of 78 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Therefore, the conference will certainly have promoted the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists). The scientific content will certainly be challenging and will promote the improvement of the work that is being developed by each of the participants.

Google Certification Guide - Google Professional Data Engineer Navigate the Data Landscape with Google Cloud Expertise Embark on a journey to become a Google Professional Data Engineer with this comprehensive guide. Tailored for data professionals seeking to leverage Google Cloud's powerful data solutions, this book provides a deep dive into the core concepts, practices, and tools necessary to excel in the field of data engineering. Inside, You'll Explore: Fundamentals to Advanced Data Concepts: Understand the full spectrum of Google Cloud data services, from BigQuery and Dataflow to AI and machine learning integrations. Practical Data Engineering Scenarios: Learn through hands-on examples and real-life case studies that demonstrate how to effectively implement data solutions on Google Cloud. Focused Exam Strategy: Prepare for the certification exam with detailed insights into the exam format, including key topics, study strategies, and practice questions. Current Trends and Best Practices: Stay abreast of the latest advancements in Google Cloud data technologies, ensuring your skills are up-to-date and industry-relevant. Authored by a Data Engineering Expert Written by an experienced data engineer, this guide bridges practical application with theoretical knowledge, offering a comprehensive and practical learning experience. Your Comprehensive Guide to Data Engineering Certification Whether you're an aspiring data engineer or an experienced professional looking to validate your Google Cloud skills, this book is an invaluable resource, guiding you through the nuances of data engineering on Google Cloud and preparing you for the Professional Data Engineer exam. Elevate Your Data Engineering Skills This guide is more than a certification prep book; it's a deep dive into the art of data engineering in the Google Cloud ecosystem, designed to equip you with advanced skills and knowledge for a successful career in data engineering. Begin Your Data Engineering Journey Step into the world of Google Cloud data engineering with confidence. This guide is your first step towards mastering the concepts and practices of data engineering and achieving certification as a Google Professional Data Engineer. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com