Data-Intensive Computing :
Data-Intensive Computing : Architectures, Algorithms, and Applications /
edited by Ian Gorton, Deborah K. Gracio.
- 1 online resource (297 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511844409 (ebook)
High performance computing
Database management
Computer storage devices
Software architecture
Data transmission systems
QA76.88 / .D38 2013
004.5
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511844409 (ebook)
High performance computing
Database management
Computer storage devices
Software architecture
Data transmission systems
QA76.88 / .D38 2013
004.5