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Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects.By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing.

RDF Database Systems is a cutting-edge guide that distills everything you need to know to effectively use or design an RDF database.This book starts with the basics of linked open data and covers the most recent research, practice, and technologies to help you leverage semantic technology.

The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines. This book covers almost all the necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc.. Ultimately, readers will be able to: understand what big data is and the factors that are involved understand the inner workings of MapReduce, which is essential for certification exams learn the features and weaknesses of MapReduce set up Hadoop clusters with 100s of physical/virtual machines create a virtual machine in AWS write MapReduce with Eclipse in a simple way understand other big data processing tools and their applications

As part of the best selling Pocket Primer series, this book is an effort to give programmers sufficient knowledge of data cleaning to be able to work on their own projects. It is designed as a practical introduction to using flexible, powerful (and free) Unix / Linux shell commands to perform common data cleaning tasks. The book is packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together. Companion files with source code are available for downloading from the publisher.Features:- A practical introduction to using flexible, powerful (and free) Unix / Linux shell commands to perform common data cleaning tasks- Includes the concept of piping data between commands, regular expression substitution, and the sed and awk commands- Packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together- Assumes the reader has no prior experience, but the topic is covered comprehensively enough to teach a pro some new tricks- Includes companion files with all of the source code examples (download from the publisher).

Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called \"seeing then doing\" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided allowing the reader to execute the scripts as they study the explanations given in the text. Features Gets you quickly using R as a problem-solving tool. Uses RStudio's integrated development environment. Shows how to interface R with SQLite. Includes examples using R's Rattle graphical user interface. Requires no prior knowledge of R, machine learning, or computer programming. Offers over 50 scripts written in R. Several of the scripts are problem-solving templates that with slight modification, can be used again and again. Covers the most popular machine learning techniques including ensemble-based methods and logistic regression. Includes end-of-chapter exercises many of which can be solved by modifying existing scripts. Includes datasets from several areas including business, health and medicine, and science.

Private clouds allow for managing multiple databases under one roof, avoiding unnecessary resource management. Private cloud solutions can be applied in sectors such as healthcare, retail, and software. The Introduction to Private Cloud using Oracle Exadata and Oracle Database will explore the general architecture of private cloud databases with a focus on Oracle’s Exadata database machine. The book describes the private cloud using fundamental-level Exadata and database. Exadata has been Oracle’s pioneer product for almost a decade. In the last few years, Oracle has positioned Exadata for customers to consume as a cloud service. This book will provide a timely introduction to Exadata for current and potential Oracle customers and other IT professionals. Introduction to Cloud Computing. Cloud Computing Architecture. Private Cloud. Relational Database Management System. Private Cloud Database Okcan Yasin Saygili is Lead Solution Architect in Turkcell. He was a cloud and database consultant at Oracle Partner HUB. His first aim is to engage Oracle partners in business development in the cloud. His second is for partners to achieve competitive database migration to the Oracle cloud/on-premise database and to deliver adoption services and business development for enterprise resource planning (ERP) to them. In other words, his objective is to increase use of the Oracle product family in the Oracle partner environment. To do so, he establishes one-to-one partner connections and performing migrations/upgrades. In addition, Yasin is responsible for project management, developing systems, database migration, adoption, and all pre-sales activities for seven countries in the partner environment. He also creates product workshops and web casts for partners and writes blogposts. Yasin was selected for inclusion in EMEA's \"Top 75 Promising Young Students\" by IBM Brussels and received executive IT training at IBM Brussels, Belgium. Yasin is an Oracle ACE. His main areas of expertise are database management and software engineering. He is also one of the management members of TROUG (Turkish Oracle User Group). Yasin is a startup project guru and has developed nearly 10 software projects, including the well-known CompileLab.

Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the ‘What’, ‘How’, and ‘Why’ of Big Data. Features Identifies the primary drivers of Big Data Walks readers through the theory, methods and technology of Big Data Explains how to handle the 4 V’s of Big Data in order to extract value for better business decision making Shows how and why data connectors are critical and necessary for Agile text analytics Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases Chapter 1: Introduction to Big Data Chapter 2: Big Data Implementation Chapter 3: Big Data Use Cases Chapter 4: Big Data Migration Chapter 5: Big Data Ingestion, Integration, and Management Chapter 6: Big Data Repository Chapter 7: Big Data Visualization Chapter 8: Structured & Un-structured Data Analytics Chapter 9: Data Virtualization Chapter 10: Cloud Computing [ Big Data: A Tutorial-Based Approach ] is a well thought-out guide, comprising of tutorials and graphic illustrations, that builds an integrated approach which clearly answers the ‘What’ and the ‘How’ and the ‘Why’ of ‘Big Data’. It takes the readers on an inquisitive journey through the information wonderland of data lakes and provides the tools and techniques to bring about the marriage of structured and unstructured data. It is a must-read primer that keeps its eyes always set on the end goal of extracting useful business insight from ‘Big Data’ by fully exploiting the potential of Hadoop Distributed File System Infrastructure, MapReduce processing, and Agile Data Analytics to implement proper Data Migration, Data Ingestion, Data Management, Data Analytics, Data Visualization and Data Virtualization processes. Last but not the least, this book finally tests the readers on their understanding of ‘Big Data’ in the form of a QUIZ. -Dr. Sohail Subhani, Winona State University Nasir Raheem is an accomplished, innovative, and results-driven project manager, architect and business analyst with over 20 years of wide-ranging experience encompassing I.T Infra-structure design, planning and implementation of highly integrated systems that included Big Data (HIVE) Database Administration, Business Re-engineering, Asset & Data management (ServiceNow), Data Integration, Data Modeling, Disaster Recovery and ERP Database /Application cloning projects. He is an experienced manager of IT projects related to multi-billion dollar corporate mergers, migration, server upgrades, database upgrades, data conversion, cloning and integration of supply chain management ERP and CRM application modules at Wells Fargo Bank, WebTV (now Microsoft) and Hitachi Global Storage Technologies (now Western Digital). He is also a published author and instructor of an online course approved by Harvard University Innovation Lab, ‘March towards Big Data.’

About the Book The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing. Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trends Target Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions. 1. Big Data Analysis in Healthcare 2. Smart EHR for Healthcare 3. Lifestyle Application for Visually Impaired 4. Classification of Genetic Mutation 5. MHealth: Community-based Android Application for Medical Services 6. Nanoemulsions: Status in Antimicrobial Therapy 7. Analysis of Air Quality and Impacts on Human Health 8. Brain Tumor Detection and Classification in MRI: Technique for Smart Healthcare Adaptation 9. Deep Strategies in Computer Assisted Diagnosis and Classification of Abnormalities in Medical Images 10. Major Histocompatibility Complex Binding and various health parameters analysis 11. Partial Digest Problem 12. Deep Learning for Next Generation Healthcare: A Survey of State-of-the-art and Research Prospects 13. Applications of Protein Nanoparticles as Drug Delivery Vehicle 14. Exploring Food Domain using Deep Neural Networks Dr. Adwitiya Sinha received her PhD from Jawaharlal Nehru University (JNU), New Delhi. She is a recipient of a Senior Research Fellowship from CSIR, New Delhi, India and a UGC Research Scholarship. Her application-based research is mainly focused on large-scale graphs, data analytics, and confluence of sensor-based applications with social networking. Megha Rathi has 10 years of teaching experience. She has worked on the Xform generator research project of at NIC, Delhi. She has experience in software development and worked as a Project Associate at IIT Delhi. Her research areas include Data Mining, Data Science Analytics, Health Science, and Machine Learning.

The easy guide to Microsoft Access returns with updates on the latest version! Microsoft Access allows you to store, organize, view, analyze, and share data; the new Access 2013 release enables you to build even more powerful, custom database solutions that integrate with the web and enterprise data sources. Access 2013 For Dummies covers all the new features of the latest version of Accessand serves as an ideal reference, combining the latest Access features with the basics of building usable databases. You'll learn how to create an app from the Welcome screen, get support for your desktop databases, and much more. Includes coverage of all the new features of Access 2013, including the updated interface Shows you how to create and share reports Features special videos and materials created by the authors to help reinforce the lessons included in the book Helps you build data analysis and interface tools for your specific needs Offers plenty of techniques and tips for solving common problems Access 2013 For Dummies provides you with access to the latest version of this database tool.