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Large-Scale Parallel Data Mining (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

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With the unprecedented rate at which data is being collected today in almost all elds of human endeavor, there is an emerging economic and scienti c need to extract useful information from it. For example, many companies already have data-warehouses in the terabyte range (e.g., FedEx, Walmart). The World Wide Web has an estimated 800 million web-pages. Similarly, scienti c data is reach- ing gigantic proportions (e.g., NASA space missions, Human Genome Project). High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalability and interactivity as datasets continue to grow in size and complexity.

To address this need we organized the workshop on Large-Scale Parallel KDD Systems, which was held in conjunction with the 5th ACM SIGKDD Interna- 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, im- plementation, and deployment of large-scale parallel knowledge discovery (PKD) systems, which can manipulate data taken from very large enterprise or scien- ti c databases, regardless of whether the data is located centrally or is globally distributed.

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