Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens. This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios. The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.

This collection of research papers provides extensive information on deploying services, concepts, and approaches for using open linked data from libraries and other cultural heritage institutions. With a special emphasis on how libraries and other cultural heritage institutions can create effective end user interfaces using open, linked data or other datasets. These papers are essential reading for any one interesting in user interface design or the semantic web.

This book discusses the recent trends and developments in the fields of information processing and information visualization. In view of the increasing amount of data, there is a need to develop visualization techniques to make that data easily understandable. Presenting such approaches from various disciplines, this book serves as a useful resource for graduates.

This book on data visualization is the eighth in a planned series of books that examine key topics (e.g., learner modeling, instructional strategies, authoring, domain modeling, assessment, team tutoring, self-improving systems, data visualization, and competency based scenario design) in intelligent tutoring system (ITS) design. This book focuses on data visualization and how it is applied in ITSs. The chapters within this book specifically examine topics in relationship to the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg & Holden, 2012; Sottilare, Brawner, Sinatra, & Johnston, 2017). GIFT is an open-source, domain-independent, modular, service-oriented architecture for ITSs. The design of GIFT allows for reusability, reduction in authoring time, and reducing the skill level needed to create an ITS. GIFT provides functionality to create ITSs, distribute ITSs to learners through the Cloud, conduct research to evaluate ITSs, and to examine instructional outcomes. Data visualization is an important topic for ITSs, as there are many different users of the systems (including learners, instructors, researchers, subject matter experts). The data that is collected by the ITS can be organized and displayed in a number of different ways. The current book includes a general discussion of how data visualizations can be applied in ITSs, as well as detailed specific examples of existing implementations, and technical details related to incorporating data visualization in ITSs. We believe this book can be used as a design tool for data visualization interfaces in ITSs.

Mobile Data Visualization is about facilitating access to and understanding of data on mobile devices. Wearable trackers, mobile phones, and tablets are used by millions of people each day to read weather maps, financial charts, or personal health meters. What is required to create effective visualizations for mobile devices? This book introduces key concepts of mobile data visualization and discusses opportunities and challenges from both research and practical perspectives. Mobile Data Visualization is the first book to provide an overview of how to effectively visualize, analyze, and communicate data on mobile devices. Drawing from the expertise, research, and experience of an international range of academics and practitioners from across the domains of Visualization, Human Computer Interaction, and Ubiquitous Computing, the book explores the challenges of mobile visualization and explains how it differs from traditional data visualization. It highlights opportunities for reaching new audiences with engaging, interactive, and compelling mobile content. In nine chapters, this book presents interesting perspectives on mobile data visualization including: how to characterize and classify mobile visualizations; how to interact with them while on the go and with limited attention spans; how to adapt them to various mobile contexts; specific methods on how to design and evaluate them; reflections on privacy, ethical and other challenges, as well as an outlook to a future of ubiquitous visualization. This accessible book is a valuable and rich resource for visualization designers, practitioners, researchers, and students alike.

Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.

This book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 11th International Conference on the Semantic Web, ESWC 2014, held in Anissaras, Crete, Greece, in May 2014. The volume contains 20 poster and 43 demonstration papers, selected from 113 submissions, as well as 12 best workshop papers selected from 60 papers presented at the workshop at ESWC 2014. Best two papers from AI Mashup Challenge are also included. The papers cover various aspects of the Semantic Web.

This volume presents the proceedings of the International Workshop on Database Issues for Data Visualization, held in conjunction with the IEEE Visualization '93 conference in San Jose, California in October 1993. The book contains 13 technical contributions organized in sections on datamodels; system integration issues; and interaction, user interfaces, and presentation issues. In addition there are three introductory section surveys and an overall workshop description summarizing the whole event. In total, the reader is presented with a thoroughly refereed and carefully edited state-of-the-art report on the hot interdisciplinary topic of database issues and data visualization.

Search computing, which has evolved from service computing, focuses on building the answers to complex search queries by interacting with a constellation of cooperating search services, using the ranking and joining of results as the dominant factors for service composition. The field is multi-disciplinary in nature and takes advantage of contributions from other research areas such as knowledge representation, human-computer interfaces, psychology, sociology, economics, and legal sciences. This book is the third in the Search Computing series and contains a collection of 16 papers, which in most cases were contributed to several workshops during 2011 organized by members of the Search Computing project in the context of major international conferences: ExploreWeb at ICWE 2011, Very Large Data Search and DBRank at VLDB 2011, DATAVIEW at ECOWS 2011, and OrdRing at ISWC 2011. The papers provide very useful insights on search computing problems and issues. The book has been divided into four parts focussing on: extraction and integration; query and visualization paradigms; exploring linked data; and games, social search and economics.

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