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Applications in Data-Intensive Computing

Authors :
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
Source :
Advances in Computers ISBN: 9780123810274
Publication Year :
2010
Publisher :
Elsevier, 2010.

Abstract

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.

Details

ISBN :
978-0-12-381027-4
ISBNs :
9780123810274
Database :
OpenAIRE
Journal :
Advances in Computers ISBN: 9780123810274
Accession number :
edsair.doi...........8d49a7008110d936b5de709ee8439430