| Autor: |
William R. Cannon, Matthew E. Monroe, Sutanay Choudhury, Andres Marquez, Todd D. Halter, Chad Scherrer, Ian Gorton, Matthew C. Macduff, Paul D. Whitney, Joshua N. Adkins, Navdeep Jaitly, William A. Pike, Deborah K. Gracio, Daniel Chavarría-Miranda, Anuj R. Shah, Christopher S. Oehmen, Douglas J. Baxter, Nino Zuljevic, Bobbie-Jo M. Webb-Robertson, Oreste Villa, Richard T. Kouzes, John R. Johnson |
| Rok vydání: |
2010 |
| Předmět: |
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| Zdroj: |
Advances in Computers ISBN: 9780123810274 |
| Popis: |
The total quantity of digital information in the world is growing at an alarming rate. Scientists and engineers are contributing heavily to this data “tsunami” by gathering data using computing and instrumentation at incredible rates. As data volumes and complexity grow, it is increasingly arduous to extract valuable information from the data and derive knowledge from that data. Addressing these demands of ever-growing data volumes and complexity requires game-changing advances in software, hardware, and algorithms. Solution technologies also must scale to handle the increased data collection and processing rates and simultaneously accelerate timely and effective analysis results. This need for ever faster data processing and manipulation as well as algorithms that scale to high-volume data sets have given birth to a new paradigm or discipline known as “data-intensive computing.” In this chapter, we define data-intensive computing, identify the challenges of massive data, outline solutions for hardware, software, and analytics, and discuss a number of applications in the areas of biology, cyber security, and atmospheric research. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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