The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study. Table of Contents: List of Figures / Introduction / Principles of Linked Data / The Web of Data / Linked Data Design Considerations / Recipes for Publishing Linked Data / Consuming Linked Data / Summary and Outlook
This book is for any manager or team leader that has the green light to implement a data governance program. The problem of managing data continues to grow with issues surrounding cost of storage, exponential growth, as well as administrative, management and security concerns - the solution to being able to scale all of these issues up is data governance which provides better services to users and saves money. What you will find in this book is an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. With the provided framework and case studies you will be enabled and educated in launching your very own successful and money saving data governance program. Provides a complete overview of the data governance lifecycle, that can help you discern technology and staff needs Specifically aimed at managers who need to implement a data governance program at their company Includes case studies to detail ´do´s´ and ´don´ts´ in real-world situations
Dieses Lehrbuch behandelt die wichtigsten Methoden zur Erkennung und Extraktion von ´´Wissen´´ aus numerischen und nicht-numerischen Datenbanken in Technik und Wirtschaft. Der Autor vermittelt einen kompakten und zugleich fundierten Überblick über die verschiedenen Methoden sowie deren Zielsetzungen und Eigenschaften. Dadurch werden Leser befähigt, Data Mining eigenständig anzuwenden.
Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that?s so clouded in hype? This insightful book, based on Columbia University?s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you?re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: * Statistical inference, exploratory data analysis, and the data science process * Algorithms * Spam filters, Naive Bayes, and data wrangling * Logistic regression * Financial modeling * Recommendation engines and causality * Data visualization * Social networks and data journalism * Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O?Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
From an award-winning project comes an inspiring, collaborative book that makes data artistic, personal - and open to all Each week for a year, Giorgia and Stefanie sent each other a postcard describing what had happened to them during that week around a particular theme. But they didn´t write it, they drew it: a week of smiling, a week of apologies, a week of desires. Presenting their fifty-two cards, along with thoughts and ideas about the data-drawing process, Dear Data hopes to inspire you to draw, slow down and make connections with other people, to see the world through a new lens, where everything and anything can be a creative starting point for play and expression.
You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop. Through several sample projects, you?ll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. You?ll also discover the features Spring Data adds to Spring?s existing JPA and JDBC support for writing RDBMS-based data access layers. * Learn about Spring?s template helper classes to simplify the use ofdatabase-specific functionality * Explore Spring Data?s repository abstraction and advanced query functionality * Use Spring Data with Redis (key/value store), HBase(column-family), MongoDB (document database), and Neo4j (graph database) * Discover the GemFire distributed data grid solution * Export Spring Data JPA-managed entities to the Web as RESTful web services * Simplify the development of HBase applications, using a lightweight object-mapping framework * Build example big-data pipelines with Spring Batch and Spring Integration