http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

Objective: To describe the promise and potential of big data analytics in healthcare. Methods: The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results: The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions: Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

/pdf/big-data-analytics-in-healthcare-promise-and-potential-525uwa75q6.pdf

Big data analytics in healthcare: promise and potential

http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. For this reason, big data implementations need to be analyzed and executed as accurately as possible. This paper presents an overview of big data's content, scope, samples, methods, advantages and challenges and discusses privacy concern on it.

Big data: A review

Big Data represents a new era in data exploration and utilization, and IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform. The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. You'll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide. Learn how IBM hardens Hadoop for enterprise-class scalability and reliability Gain insight into IBM's unique in-motion and at-rest Big Data analytics platform Learn tips and tricks for Big Data use cases and solutions Get a quick Hadoop primer

Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data

Design and deploy integrated enterprise-class business solutions
Enhance productivity, profitability, and efficiency using IBM DB2 9 and the detailed information in this comprehensive resource. Written by experienced IBM data server professionals, IBM DB2 9 New Features covers the technical details of the major new aspects of DB2 9 alongside real-world business uses, syntax examples, and insider tips. Learn how to manipulate and manage XML data using pureXML, automate maintenance tasks, and seamlessly integrate Java, PHP, and Perl scripts. Plus, you'll get vital coverage of the brand-new DB2 Developer Workbench, enhanced .NET developer support, Ruby on Rails, and database security.

Learn about all the auto-configuration features that make getting DB2 up and running faster than ever before
Define, share, and validate data using XML and the unique pureXML DB2 Hybrid Storage Engine
Improve runtime performance and optimize storage with the new DB2 Compression Technology
Automatically handle DB2 memory allocation using the Self Tuning Memory Manager
Use Automatic Storage, Autonomic Computing, and a host of other autonomic features that make managing DB2 a snap
Create dynamic data server blueprints with Rational Data Architect version 6.1
Incorporate Java applications using the IBM WebSphere Application Server and DB2 data server
Develop open-source scripts using PHP, Perl, Toad, and Ruby on Rails
Utilize the DB2 Developer Workbench or exploit the enhanced .NET developer support
Deploy the latest DB2 security and high availability features


Table of contents

Part I. XML in DB2 New Hybrid Storage Engine
Ch. 1. What is XML?
Ch. 2. When Do I use pureXML?
Ch. 3. pureXML: The DB2 XML Database
Ch. 4. Tuning XML Performance
Part II. Autonomics and Manageability
Ch. 5. Up and Running Enhancements
Ch. 6. Self Tuning Memory Manager
Ch. 7. Automated Storage
Ch. 8. Other Autonomic and Manageability Enhancements
Part III. Application Development
Ch. 9. Rational Data Architect
Ch. 10. DB2 and the .NET Developer
Ch. 11. DB2 and the Java Developer
Ch. 12. DB2 and the Scripting Languages Developer
Ch. 13. DB2 and the XML Developer
Ch. 14. DB2 Developer Workbench
Ch. 15. Other Application Development Topics
Part IV. Security Enhancements
Ch. 16. Label-Based Access Control
Ch. 17. Additional Security Enhancements
Part V. SHARP Enhancements
Ch. 18. Larger Table Space Support
Ch. 19. Larger Object Support
Ch. 20. Raw Compression
Ch. 21. High-Availability Enhancements
Ch. 22. Additional SHARP Enhancements
Index

Chris Eaton

Papers

Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data

IBM DB2 9 New Features