TY  - BOOK
AU  - Gorton,Ian
AU  - Gracio,Deborah K.
TI  - Data-intensive computing: architectures, algorithms, and applications 
SN  - 9780511844409 (ebook)
AV  - QA76.88 .D38 2013
U1  - 004.5 23
PY  - 2013///
CY  - Cambridge
PB  - Cambridge University Press
KW  - High performance computing
KW  - Database management
KW  - Computer storage devices
KW  - Software architecture
KW  - Data transmission systems
N1  - Title from publisher's bibliographic system (viewed on 05 Oct 2015); 1. Data-intensive computing: a challenge for the 21st century / Ian Gorton and Deborah K. Gracio -- 2. The anatomy of data-intensive computing applications / Ian Gorton and Deborah K. Gracio -- 3. Hardware architectures for data-intensive computing problems: a case study for string matching / Antonino Tumeo, Oreste Villa and Daniel Chavarría-Miranda -- 4. Data management architectures / Terence Critchlow, Ghaleb Abdulla, Jacek Becla, Kerstin Kleese-Van Dam, Sam Lang and Deborah L. McGuinness -- 5. Large scale data management techniques in cloud computing platforms / Sherif Sakr and Anna Liu -- 6. Dimension reduction for streaming data / Chandrika Kamath -- 7. Binary classification with support vector machines / Patrick Nichols, Bobbie-Jo Webb-Robertson and Christopher Oehmen -- 8. Beyond MapReduce: new requirements for scalable data processing / Bill Howe -- 9. Letting the data do the talking: hypothesis discovery from large-scale datasets in real time / Christopher Oehmen, Scott Dowson, Wes Hatley, Justin Almquist, Bobbie-Jo Webb-Robertson, Jason McDermott, Ian Gorton and Lee Ann McCue -- 10. Data-intensive visual analysis for cybersecurity / William A. Pike, Daniel M. Best, Douglas V. Love and Shawn J. Bohn
N2  - 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
UR  - https://doi.org/10.1017/CBO9780511844409
ER  -