Large-Scale Parallel Data Mining

Name: Large-Scale Parallel Data Mining
Brand: Springer, Berlin
SKU: 9783540671947
Price: 50.14 EUR
Availability: InStock

Autor: Mohammed J. Zaki, Ching-Tien Ho

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer, Berlin

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Withthe unprecedented rate at which data is being collected today in almostall elds of human endeavo...

Informácie o knihe

Autor

Mohammed J. Zaki, Ching-Tien Ho

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2000

Stránok

260

EAN

9783540671947

ISBN

3540671943

Enbook ID

02710711

Vydavateľ

Springer, Berlin

Hmotnosť

426

Rozmery

155 x 233 x 14

Kompletný popis

Withthe unprecedented rate at which data is being collected today in almostall elds of human endeavor, there is an emerging economic and scientic need to extract useful information from it. For example, many companies already have data-warehouses inthe terabyte range (e.g., FedEx, Walmart).The WorldWide Web has an estimated 800 millionweb-pages. Similarly,scienti c data is rea- ing gigantic proportions (e.g., NASA space missions, Human Genome Project). High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalabilityand interactivityas datasets continue to grow in size and complexity. Toaddress thisneedweorganizedtheworkshoponLarge-ScaleParallelKDD Systems, which was held in conjunction with the 5th ACM SIGKDD Inter- tional Conference on Knowledge Discovery and Data Mining, on August 15th, 1999, San Diego, California. The goal of this workshop was to bring researchers and practitioners together in a setting where they could discuss the design, - plementation,anddeploymentoflarge-scaleparallelknowledgediscovery (PKD) systems, which can manipulate data taken from very large enterprise or sci- tic databases, regardless of whether the data is located centrally or is globally distributed. Relevant topics identie d for the workshop included: { How to develop a rapid-response, scalable, and parallel knowledge discovery system that supports global organizations with terabytes of data.

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