Parallel CBAR Technique in Hadoop-MapReduce Framework (Paperback)
Sayantan Singha Roy
ISBN 10: 3659912751 ISBN 13: 9783659912757
Published by LAP Lambert Academic Publishing, 2024
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Paperback. Data clustering is a prevalent challenge in big data processing, and parallelizing clustering operations significantly enhances efficiency in applications involving frequent searches. Various clustering techniques are available for data grouping, with CBAR being widely used across different applications. Parallelizing CBAR is essential for big data, and the Hadoop MapReduce platform offers a suitable framework to improve efficiency by leveraging effective segmentation techniques. This book involves designing and implementing algorithms for CBAR using the MapReduce approach, with testing conducted on clusters of up to 4 nodes. The results demonstrate substantial performance gains, which are analyzed and discussed with illustrative examples. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783659912757
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Data clustering is a prevalent challenge in big data processing, and parallelizing clustering operations significantly enhances efficiency in applications involving frequent searches. Various clustering techniques are available for data grouping, with CBAR being widely used across different applications. Parallelizing CBAR is essential for big data, and the Hadoop MapReduce platform offers a suitable framework to improve efficiency by leveraging effective segmentation techniques. This book involves designing and implementing algorithms for CBAR using the MapReduce approach, with testing conducted on clusters of up to 4 nodes. The results demonstrate substantial performance gains, which are analyzed and discussed with illustrative examples.
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Title: Parallel CBAR Technique in Hadoop-MapReduce ...
Publisher: LAP Lambert Academic Publishing
Publication Date: 2024
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Taschenbuch. Condition: Neu. Neuware -Data clustering is a prevalent challenge in big data processing, and parallelizing clustering operations significantly enhances efficiency in applications involving frequent searches. Various clustering techniques are available for data grouping, with CBAR being widely used across different applications. Parallelizing CBAR is essential for big data, and the Hadoop MapReduce platform offers a suitable framework to improve efficiency by leveraging effective segmentation techniques. This book involves designing and implementing algorithms for CBAR using the MapReduce approach, with testing conducted on clusters of up to 4 nodes. The results demonstrate substantial performance gains, which are analyzed and discussed with illustrative examples.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Seller Inventory # 9783659912757
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Parallel CBAR Technique in Hadoop-MapReduce Framework (Paperback)
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ISBN 10: 3659912751
ISBN 13: 9783659912757
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Paperback. Condition: new. Paperback. Data clustering is a prevalent challenge in big data processing, and parallelizing clustering operations significantly enhances efficiency in applications involving frequent searches. Various clustering techniques are available for data grouping, with CBAR being widely used across different applications. Parallelizing CBAR is essential for big data, and the Hadoop MapReduce platform offers a suitable framework to improve efficiency by leveraging effective segmentation techniques. This book involves designing and implementing algorithms for CBAR using the MapReduce approach, with testing conducted on clusters of up to 4 nodes. The results demonstrate substantial performance gains, which are analyzed and discussed with illustrative examples. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9783659912757
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Parallel CBAR Technique in Hadoop-MapReduce Framework
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ISBN 10: 3659912751
ISBN 13: 9783659912757
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Parallel CBAR Technique in Hadoop-MapReduce Framework
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ISBN 13: 9783659912757
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