Isbn: 9783540671947 - large-scale parallel data mining (lecture notes in computer science / lecture notes in artificial intelligence) (lecture notes in computer science, 1759, band 1759) (4 Ergebnisse)

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  • Sprache: Englisch

    Verlag: Springer Berlin / Heidelberg, 2000

    3540671943 / 9783540671947

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    Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

  • Sprache: Englisch

    Verlag: Springer, 2000

    3540671943 / 9783540671947

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  • Sprache: Englisch

    Verlag: Springer, 2000

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  • Sprache: Englisch

    Verlag: Springer, 2000

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    Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.