Describes principles of the emerging field of data-intensive computing, along with methods for designing, managing and analyzing the big data sets of today.
Inhaltsverzeichnis
1. Data-intensive computing: a challenge for the twenty-first 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 Chavarr a-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 data sets 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.
