A Distributed HOSVD Method With Its Incremental Computation for Big Data in Cyber-Physical-Social Systems
A Distributed HOSVD Method With Its Incremental Computation for Big Data in Cyber-Physical-Social Systems
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DOI：
10.1109/tcss.2018.2813320
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发表时间：
2018-05
影响因子：
5
通讯作者：
Xiaokang Wang;Wei Wang;L. Yang;Siwei Liao;Dexiang Yin;M. Deen
中科院分区：
文献类型：
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作者：
Xiaokang Wang;Wei Wang;L. Yang;Siwei Liao;Dexiang Yin;M. Deen
网络-物理-社会系统(CPSS)将网络、物理和社会空间整合在一起，给人类带来了便利和挑战。为了实际应用和方便用户，对CPSS产生的大数据进行实时处理是必不可少的。因此，大数据计算在处理周期性传入数据时，应避免对历史数据进行重复计算。本文提出了一种列式高阶奇异值分解算法(HOSVD)来实现张量表示的大数据的降维、提取和降噪。首先，提出了基于列式雅可比的分布式HOSVD算法(DHOSVD)，实现了HOSVD算法的分布式计算。其次，连续产生大流数据，中间结果可以被记录用于下一计算步骤。第三，我们提出了一种类似的列式增量HOSVD(IHOSVD)方案来支持时间增量数据流的在线计算。两个基于HOSVD的方案的性能将说明我们的高效实时大数据处理方法的可扩展性。
Cyber-physical-social systems (CPSS), integrating cyber, physical, and social spaces together, bring both conveniences and challenges to humans. For practical applications and user convenience, it is essential that the Big Data produced in CPSS be processed in real time. Therefore, Big Data computation should avoid redundant computations on historical data when dealing with periodic incoming data. In this paper, we propose a columnwise high-order singular value decomposition (HOSVD) algorithm to realize dimensionality reduction, extraction, and noise reduction for tensor-represented Big Data. First, the distributed HOSVD (DHOSVD) is proposed using the columnwise Jacobi-based approach to realize the distributed computation of HOSVD. Second, big streaming data are continuously produced and the intermediate results could be recorded for the next computational step. Third, we propose a similar columnwise incremental HOSVD (IHOSVD) scheme to support online computation on temporally incremental data streaming. The performance of the two HOSVD-based schemes will illustrate the scalability of our efficient real-time Big Data processing methods.