The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.They uploaded the source data to S3 and started a Hadoop cluster of customized EC2 Amazon Machine Images (AMIs). With one hundred EC2 AMIs running in parallel, it was possible to complete the task of reading the source data from S3, ... AzureBlast  is a system that is designed to show the applicability of cloud platforms for science applications. ... such as: 0 Reduced time-to-market by removing or simplifying the time-consuming hardware provisioning, purchasing, andanbsp;...
|Author||:||Ian Gorton, Deborah K. Gracio|
|Publisher||:||Cambridge University Press - 2012-10-29|