Network Graph Analysis and Visualization with Gephi


Author: Ken Cherven
Publisher: Packt Publishing Ltd
ISBN: 178328014X
Category: Computers
Page: 116
View: 5818
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A practical, hands-on guide, that provides you with all the tools you need to visualize and analyze your data using network graphs with Gephi.This book is for data analysts who want to intuitively reveal patterns and trends, highlight outliers, and tell stories with their data using Gephi. It is great for anyone looking to explore interactions within network datasets, whether the data comes from social media or elsewhere. It is also a valuable resource for those seeking to learn more about Gephi without being overwhelmed by technical details.

Gephi Cookbook


Author: Devangana Khokhar
Publisher: Packt Publishing Ltd
ISBN: 1783987413
Category: Computers
Page: 296
View: 3798
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If you want to learn network analysis and visualization along with graph concepts from scratch, then this book is for you. This is ideal for those of you with little or no understanding of Gephi and this domain, but will also be beneficial for those interested in expanding their knowledge and experience.

Complex Network Analysis in Python

Recognize - Construct - Visualize - Analyze - Interpret
Author: Dmitry Zinoviev
Publisher: Pragmatic Bookshelf
ISBN: 1680505408
Category: Computers
Page: 262
View: 3182
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Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

Visualizing Graph Data


Author: Corey Lanum
Publisher: Manning Publications
ISBN: 9781617293078
Category: Computers
Page: 232
View: 2719
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Summary Visualizing Graph Data teaches you not only how to build graph data structures, but also how to create your own dynamic and interactive visualizations using a variety of tools. This book is loaded with fascinating examples and case studies to show you the real-world value of graph visualizations. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Assume you are doing a great job collecting data about your customers and products. Are you able to turn your rich data into important insight? Complex relationships in large data sets can be difficult to recognize. Visualizing these connections as graphs makes it possible to see the patterns, so you can find meaning in an otherwise over-whelming sea of facts. About the Book Visualizing Graph Data teaches you how to understand graph data, build graph data structures, and create meaningful visualizations. This engaging book gently introduces graph data visualization through fascinating examples and compelling case studies. You'll discover simple, but effective, techniques to model your data, handle big data, and depict temporal and spatial data. By the end, you'll have a conceptual foundation as well as the practical skills to explore your own data with confidence. What's Inside Techniques for creating effective visualizations Examples using the Gephi and KeyLines visualization packages Real-world case studies About the Reader No prior experience with graph data is required. About the Author Corey Lanum has decades of experience building visualization and analysis applications for companies and government agencies around the globe. Table of Contents PART 1 - GRAPH VISUALIZATION BASICS Getting to know graph visualization Case studies An introduction to Gephi and KeyLines PART 2 VISUALIZE YOUR OWN DATA Data modeling How to build graph visualizations Creating interactive visualizations How to organize a chart Big data: using graphs when there's too much data Dynamic graphs: how to show data over time Graphs on maps: the where of graph visualization

Twitter

Social Communication in the Twitter Age
Author: Dhiraj Murthy
Publisher: John Wiley & Sons
ISBN: 0745665101
Category: Computers
Page: 220
View: 1673
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Twitter has become a household name, discussed both for its role in prominent national elections, natural disasters, and political movements, as well as for what some malign as narcissistic “chatter.” This book takes a critical step back from popular discourse and media coverage of Twitter, to present the first balanced, scholarly engagement of this popular medium. In this timely and comprehensive introduction, Murthy not only discusses Twitter’s role in our political, economic, and social lives, but also draws a historical line between the telegraph and Twitter to reflect on changes in social communication over time. The book thoughtfully examines Twitter as an emergent global communications medium and provides a theoretical framework for students, scholars, and tweeters to reflect critically on the impact of Twitter and the contemporary media environment. The book uses case studies including citizen journalism, health, and national disasters to provide empirically rich insights and to help decipher some of the ways in which Twitter and social media more broadly may be shaping contemporary life.

Analyzing the Social Web


Author: Jennifer Golbeck
Publisher: Newnes
ISBN: 0124058566
Category: Computers
Page: 290
View: 1452
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Analyzing the Social Web provides a framework for the analysis of public data currently available and being generated by social networks and social media, like Facebook, Twitter, and Foursquare. Access and analysis of this public data about people and their connections to one another allows for new applications of traditional social network analysis techniques that let us identify things like who are the most important or influential people in a network, how things will spread through the network, and the nature of peoples' relationships. Analyzing the Social Web introduces you to these techniques, shows you their application to many different types of social media, and discusses how social media can be used as a tool for interacting with the online public. Presents interactive social applications on the web, and the types of analysis that are currently conducted in the study of social media. Covers the basics of network structures for beginners, including measuring methods for describing nodes, edges, and parts of the network. Discusses the major categories of social media applications or phenomena and shows how the techniques presented can be applied to analyze and understand the underlying data. Provides an introduction to information visualization, particularly network visualization techniques, and methods for using them to identify interesting features in a network, generate hypotheses for analysis, and recognize patterns of behavior. Includes a supporting website with lecture slides, exercises, and downloadable social network data sets that can be used can be used to apply the techniques presented in the book.

Mastering Machine Learning with Spark 2.x


Author: Alex Tellez,Max Pumperla,Michal Malohlava
Publisher: Packt Publishing Ltd
ISBN: 1785282417
Category: Computers
Page: 340
View: 8985
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Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment. Style and approach This book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of machine learning, through Spark, in a variety of enterprise-grade systems.

The Connected Past

Challenges to Network Studies in Archaeology and History
Author: Tom Brughmans,Anna Collar,Fiona Coward
Publisher: Oxford University Press
ISBN: 0191065382
Category: Social Science
Page: 240
View: 6775
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One of the most exciting recent developments in archaeology and history has been the adoption of new perspectives which see human societies in the past—as in the present—as made up of networks of interlinked individuals. This view of people as always connected through physical and conceptual networks along which resources, information, and disease flow, requires archaeologists and historians to use new methods to understand how these networks form, function, and change over time. The Connected Past provides a constructive methodological and theoretical critique of the growth in research applying network perspectives in archaeology and history, and considers the unique challenges presented by datasets in these disciplines, including the fragmentary and material nature of such data and the functioning and change of social processes over long timespans. An international and multidisciplinary range of scholars debate both the rationale and practicalities of applying network methodologies, addressing the merits and drawbacks of specific techniques of analysis for a range of datasets and research questions, and demonstrating their approaches with concrete case studies and detailed illustrations. As well as revealing the valuable contributions archaeologists and historians can make to network science, the volume represents a crucial step towards the development of best practice in the field, especially in exploring the interactions between social and material elements of networks, and long-term network evolution.

Practical Data Analysis


Author: Hector Cuesta,Dr. Sampath Kumar
Publisher: Packt Publishing Ltd
ISBN: 1785286668
Category: Computers
Page: 338
View: 5846
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A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Mining the Social Web

Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
Author: Matthew A. Russell,Matthew Russell
Publisher: "O'Reilly Media, Inc."
ISBN: 1449388345
Category: Computers
Page: 332
View: 3157
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Provides information on data analysis from a vareity of social networking sites, including Facebook, Twitter, and LinkedIn.

Social and Economic Networks


Author: Matthew O. Jackson
Publisher: Princeton University Press
ISBN: 9781400833993
Category: Business & Economics
Page: 520
View: 6003
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Networks of relationships help determine the careers that people choose, the jobs they obtain, the products they buy, and how they vote. The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function. This book is an indispensable resource for students and researchers in economics, mathematics, physics, sociology, and business.

Foundations of Modern Networking

SDN, NFV, QoE, IoT, and Cloud
Author: William Stallings
Publisher: Addison-Wesley Professional
ISBN: 0134176022
Category: Computers
Page: 544
View: 9960
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Foundations of Modern Networking is a comprehensive, unified survey of modern networking technology and applications for today’s professionals, managers, and students. Dr. William Stallings offers clear and well-organized coverage of five key technologies that are transforming networks: Software-Defined Networks (SDN), Network Functions Virtualization (NFV), Quality of Experience (QoE), the Internet of Things (IoT), and cloudbased services. Dr. Stallings reviews current network ecosystems and the challenges they face–from Big Data and mobility to security and complexity. Next, he offers complete, self-contained coverage of each new set of technologies: how they work, how they are architected, and how they can be applied to solve real problems. Dr. Stallings presents a chapter-length analysis of emerging security issues in modern networks. He concludes with an up-to date discussion of networking careers, including important recent changes in roles and skill requirements. Coverage: Elements of the modern networking ecosystem: technologies, architecture, services, and applications Evolving requirements of current network environments SDN: concepts, rationale, applications, and standards across data, control, and application planes OpenFlow, OpenDaylight, and other key SDN technologies Network functions virtualization: concepts, technology, applications, and software defined infrastructure Ensuring customer Quality of Experience (QoE) with interactive video and multimedia network traffic Cloud networking: services, deployment models, architecture, and linkages to SDN and NFV IoT and fog computing in depth: key components of IoT-enabled devices, model architectures, and example implementations Securing SDN, NFV, cloud, and IoT environments Career preparation and ongoing education for tomorrow’s networking careers Key Features: Strong coverage of unifying principles and practical techniques More than a hundred figures that clarify key concepts Web support at williamstallings.com/Network/ QR codes throughout, linking to the website and other resources Keyword/acronym lists, recommended readings, and glossary Margin note definitions of key words throughout the text

Learning Neo4j


Author: Rik Van Bruggen
Publisher: Packt Publishing Ltd
ISBN: 1849517177
Category: Computers
Page: 222
View: 1715
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This book is for developers who want an alternative way to store and process data within their applications. No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily.

Computer Modelling for Sustainable Urban Design

Physical Principles, Methods and Applications
Author: Darren Robinson
Publisher: Routledge
ISBN: 1136539352
Category: Political Science
Page: 320
View: 3691
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This is the first book to directly address the physics of urban sustainability and how urban sustainability may be modelled and optimised. Starting with an introduction to the importance and key aspects of the topic, it moves on to a detailed consideration of the urban climate and pedestrian comfort. Comprehensive techniques for the modelling and optimisation of urban metabolism are then described, together with means for defining sustainability as the fitness function to be optimised. It ends with an eye to the future of sustainable urban design and the means available to urban designers and governors to help them to secure a more sustainable urban future. This book will be invaluable both in informing the next generation of urban planners, architects and engineers, and as a tool to current professionals that will directly contribute to the effectiveness of their work by allowing them to more successfully measure and model urban sustainability.

The Philosophical Breakfast Club

Four Remarkable Friends who Transformed Science and Changed the World
Author: Laura J. Snyder
Publisher: Random House Digital, Inc.
ISBN: 0767930495
Category: Biography & Autobiography
Page: 439
View: 548
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Snyder delivers a compelling portrait of four remarkable friends--William Whewell, Charles Babbage, John Herschel, and Richard Jones--who transformed science and changed the world.

Social Media Campaigns

Strategies for Public Relations and Marketing
Author: Carolyn Mae Kim
Publisher: Routledge
ISBN: 1317312880
Category: Business & Economics
Page: 182
View: 8668
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Social media has ushered in a new era of communication between organizations and key stakeholders. This text guides readers through a four-step process of developing a robust social media campaign. Covering the latest industry standards and best practices to engage digital audiences through social listening, strategic design, creative engagement and evaluation, each chapter also includes expert insights from social media professionals. Focusing on principles rather than a specific platform, this is a text dedicated to developing social media competency that can adapt to any organization or environment.

Commercial Data Mining

Processing, Analysis and Modeling for Predictive Analytics Projects
Author: David Nettleton
Publisher: Elsevier
ISBN: 012416658X
Category: Computers
Page: 304
View: 1878
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Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levels Includes practical examples and case studies as well as actionable business insights from author's own experience

R Data Visualization Cookbook


Author: Atmajitsinh Gohil
Publisher: Packt Publishing Ltd
ISBN: 1783989513
Category: Computers
Page: 236
View: 9959
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If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.

Data Science Essentials in Python

Collect - Organize - Explore - Predict - Value
Author: Dmitry Zinoviev
Publisher: Pragmatic Bookshelf
ISBN: 1680503383
Category: Business & Economics
Page: 226
View: 4825
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Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.

Mastering Structured Data on the Semantic Web

From HTML5 Microdata to Linked Open Data
Author: Leslie Sikos
Publisher: Apress
ISBN: 1484210492
Category: Computers
Page: 256
View: 5538
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A major limitation of conventional web sites is their unorganized and isolated contents, which is created mainly for human consumption. This limitation can be addressed by organizing and publishing data, using powerful formats that add structure and meaning to the content of web pages and link related data to one another. Computers can "understand" such data better, which can be useful for task automation. The web sites that provide semantics (meaning) to software agents form the Semantic Web, the Artificial Intelligence extension of the World Wide Web. In contrast to the conventional Web (the "Web of Documents"), the Semantic Web includes the "Web of Data", which connects "things" (representing real-world humans and objects) rather than documents meaningless to computers. Mastering Structured Data on the Semantic Web explains the practical aspects and the theory behind the Semantic Web and how structured data, such as HTML5 Microdata and JSON-LD, can be used to improve your site’s performance on next-generation Search Engine Result Pages and be displayed on Google Knowledge Panels. You will learn how to represent arbitrary fields of human knowledge in a machine-interpretable form using the Resource Description Framework (RDF), the cornerstone of the Semantic Web. You will see how to store and manipulate RDF data in purpose-built graph databases such as triplestores and quadstores, that are exploited in Internet marketing, social media, and data mining, in the form of Big Data applications such as the Google Knowledge Graph, Wikidata, or Facebook’s Social Graph. With the constantly increasing user expectations in web services and applications, Semantic Web standards gain more popularity. This book will familiarize you with the leading controlled vocabularies and ontologies and explain how to represent your own concepts. After learning the principles of Linked Data, the five-star deployment scheme, and the Open Data concept, you will be able to create and interlink five-star Linked Open Data, and merge your RDF graphs to the LOD Cloud. The book also covers the most important tools for generating, storing, extracting, and visualizing RDF data, including, but not limited to, Protégé, TopBraid Composer, Sindice, Apache Marmotta, Callimachus, and Tabulator. You will learn to implement Apache Jena and Sesame in popular IDEs such as Eclipse and NetBeans, and use these APIs for rapid Semantic Web application development. Mastering Structured Data on the Semantic Web demonstrates how to represent and connect structured data to reach a wider audience, encourage data reuse, and provide content that can be automatically processed with full certainty. As a result, your web contents will be integral parts of the next revolution of the Web.