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Titlebook: Big Data Concepts, Theories, and Applications; Shui Yu,Song Guo Book 2016 Springer International Publishing Switzerland 2016 Big data.Big

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發(fā)表于 2025-3-23 12:27:41 | 只看該作者
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發(fā)表于 2025-3-23 14:08:21 | 只看該作者
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發(fā)表于 2025-3-23 20:02:42 | 只看該作者
https://doi.org/10.1007/978-3-540-72726-2o the labeling mistake and complex composition of big Internet data. This chapter further evaluates the performance of typical machine learning algorithms with unclean training data. The review and the empirical study can provide a guide for academia and practitioners in choosing proper traffic classification methods in real-world scenarios.
14#
發(fā)表于 2025-3-24 00:36:04 | 只看該作者
Bayesian Single Target Tracking,of-the-art data-leak detection designs and algorithms. These solutions leverage big data theories and platforms—data mining, MapReduce, GPGPU, etc.—to harden the privacy control for big data. We also discuss the open research problems in data-leak detection and prevention.
15#
發(fā)表于 2025-3-24 03:58:10 | 只看該作者
Definitions and Conceptual Orientationsthem on different cluster settings that consist of three, six, and nine slave nodes and a single mater node in our testbed. Our data shows these attacks can affect the performance of MapReduce by increasing the computing time of MapReduce jobs.
16#
發(fā)表于 2025-3-24 06:52:37 | 只看該作者
Traffic Identification in Big Internet Data,o the labeling mistake and complex composition of big Internet data. This chapter further evaluates the performance of typical machine learning algorithms with unclean training data. The review and the empirical study can provide a guide for academia and practitioners in choosing proper traffic classification methods in real-world scenarios.
17#
發(fā)表于 2025-3-24 12:12:02 | 只看該作者
18#
發(fā)表于 2025-3-24 18:48:23 | 只看該作者
Cyber Attacks on MapReduce Computation Time in a Hadoop Cluster,them on different cluster settings that consist of three, six, and nine slave nodes and a single mater node in our testbed. Our data shows these attacks can affect the performance of MapReduce by increasing the computing time of MapReduce jobs.
19#
發(fā)表于 2025-3-24 19:45:15 | 只看該作者
20#
發(fā)表于 2025-3-24 23:55:03 | 只看該作者
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