Abstract
Cyber-Physical-Social Systems (CPSS) integrating the cyber, physical, and social worlds is a key technology to provide proactive and personalized services for humans. In this paper, we studied CPSS by taking human-interaction-aware big data (HIBD) as the starting point. However, the HIBD collected from all aspects of our daily lives are of high-order and large-scale, which bring ever-increasing challenges for their cleaning, integration, processing, and interpretation. Therefore, new strategies for representing and processing of HIBD become increasingly important in the provision of CPSS services. As an emerging technique, tensor is proving to be a suitable and promising representation and processing tool of HIBD. In particular, tensor networks, as a significant tensor decomposition technique, bring advantages of computing, storage, and applications of HIBD. Furthermore, Tensor-Train (TT), a type of tensor network, is particularly well suited for representing and processing high-order data by decomposing a high-order tensor into a series of low-order tensors. However, at present, there is still need for an efficient Tensor-Train decomposition method for massive data. Therefore, for larger-scale HIBD, a highly-efficient computational method of Tensor-Train is required. In this paper, a distributed Tensor-Train (DTT) decomposition method is proposed to process the high-order and large-scale HIBD. The high performance of the proposed DTT such as the execution time is demonstrated with a case study on a typical form of CPSS data, Computed Tomography (CT) image data.
- Andrzej Cichocki. 2014. Era of big data processing: A new approach via tensor networks and tensor decompositions. arxiv preprint arxiv:1403.2048 (2014). https://arxiv.org/pdf/1403.2048.pdf.Google Scholar
- Lieven De Lathauwer, Bart De Moor, and Joos Vandewalle. 2000. A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21, 4 (2000), 1253--1278.Google ScholarDigital Library
- Heng Huang, Chris Ding, Dijun Luo, and Tao Li. 2008. Simultaneous tensor subspace selection and clustering: The equivalence of high order SVD and k-means clustering. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 327--335.Google ScholarDigital Library
- Bo Jin, Tran Hoai Thu, Eunhye Baek, Sung Hwan Sakong, Jin Xiao, Tapas Mondal, and M. Jamal Deen. 2014. Walking-age analyzer for healthcare applications. IEEE J. Biomed. Health Inf. 18, 3 (2014), 1034--1042.Google ScholarCross Ref
- Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Rev. 51, 3 (2009), 455--500.Google ScholarDigital Library
- Junli Liang, Yang He, Ding Liu, and Xianju Zeng. 2012. Image fusion using higher order singular value decomposition. IEEE Transa. Image Process. 21, 5 (2012), 2898--2909.Google ScholarDigital Library
- Sumit Majumder, Tapas Mondal, and M. Deen. 2017. Wearable sensors for remote health monitoring. Sensors 17, 1 (2017), 130.Google ScholarCross Ref
- Surya Nepal, Rajiv Ranjan, and Kim-Kwang Raymond Choo. 2015. Trustworthy processing of healthcare big data in hybrid clouds. IEEE Cloud Comput. 2, 2 (2015), 78--84.Google ScholarCross Ref
- Deepak Puthal, Surya Nepal, Rajiv Ranjan, and Jinjun Chen. 2015. A dynamic key length based approach for real-time security verification of big sensing data stream. In Proceedings of the International Conference on Web Information Systems Engineering. Springer, 93--108.Google ScholarCross Ref
- Rajiv Ranjan and Liang Zhao. 2013. Peer-to-peer service provisioning in cloud computing environments. J. Supercomput. 65, 1 (2013), 154--184.Google ScholarDigital Library
- M. Alex O. Vasilescu. 2002. Human motion signatures: Analysis, synthesis, recognition. In Proceedings of the 16th International Conference on Pattern Recognition. Quebec, Canada, 456--460. http://alumni.media.mit.edu/∼maov/motionsignatures/hms_icpr02_corrected.pdf.Google ScholarCross Ref
- Deo Prakash Vidyarthi, Biplab Kumer Sarker, Anil Kumar Tripathi, and Laurence Tianruo Yang. 2008. Scheduling in Distributed Computing Systems: Analysis, Design and Models. Springer Science 8 Business Media.Google Scholar
- Xiaokang Wang, Laurence T. Yang, Xingyu Chen, Lizhe Wang, Rajiv Ranjan, Xiaodao Chen, and M. Jamal Deen. 2018. A multi-order distributed HOSVD with its incremental computing for big services in cyber-physical-social systems. IEEE Trans. Big Data. DOI:10.1109/TBDATA.2018.2824303 (2018). https://ieeexplore.ieee.org/abstract/document/8333789.Google Scholar
- Xiaokang Wang, Laurence T. Yang, Huazhong Liu, and M. Jamal Deen. 2016. A big data-as-a-service framework: State-of-the-art and perspectives. IEEE Trans. Big Data 4, 3 (2016), 325--340.Google ScholarCross Ref
- Xiaokang Wang, Laurence T. Yang, Xia Xie, Jirong Jin, and M. Jamal Deen. 2017. A cloud-edge computing framework for cyber-physical-social services. IEEE Commun. Mag. 55, 11 (2017), 80--85.Google ScholarCross Ref
- Yu Wang, Yang Xiang, Jun Zhang, Wanlei Zhou, Guiyi Wei, and Laurence T Yang. 2013. Internet traffic classification using constrained clustering. IEEE Trans. Parallel Distrib. Syst. 25, 11 (2013), 2932--2943.Google ScholarCross Ref
- Naixue Xiong, Athanasios V. Vasilakos, Laurence T. Yang, Cheng-Xiang Wang, Rajgopal Kannan, Chin-Chen Chang, and Yi Pan. 2010. A novel self-tuning feedback controller for active queue management supporting TCP flows. Inf. Sci. 180, 11 (2010), 2249--2263.Google ScholarDigital Library
- Wei Xiong, Hanping Hu, Naixue Xiong, Laurence T. Yang, Wen-Chih Peng, Xiaofei Wang, and Yanzhen Qu. 2014. Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications. Inf. Sci. 258 (2014), 403--415. https://ir.nctu.edu.tw/bitstream/11536/23369/1/000329262200027.pdf.Google ScholarDigital Library
- Qiang Yang, Chunzhi Hu, and Nenggan Zheng. 2018. Data-driven diagnosis of nonlinearly mixed mechanical faults in wind turbine gearbox. IEEE Iof T J. 5, 1 (2018), 466--467.Google Scholar
- Jing Zeng, Laurence T. Yang, Man Lin, Huansheng Ning, and Jianhua Ma. 2016. A survey: Cyber-physical-social systems and their system-level design methodology. Fut. Gener. Comput. Syst. https://doi.org/10.1016/j.future.2016.06.034 (2016). https://www.sciencedirect.com/science/article/pii/S0167739X1630228X.Google Scholar
- Daqiang Zhang, Daqing Zhang, Haoyi Xiong, Laurence T. Yang, and Vincent Gauthier. 2015. NextCell: Predicting location using social interplay from cell phone traces. IEEE Trans. Comput. 64, 2 (2015), 452--463.Google ScholarDigital Library
- Yaliang Zhao, Laurence T. Yang, and Ronghao Zhang. 2018. A tensor-based multiple clustering approach with its applications in automation systems. IEEE Trans. Industr. Inf. 14, 1 (2018), 283--291.Google ScholarCross Ref
- Guoxu Zhou, Andrzej Cichocki, and Shengli Xie. 2014. Decomposition of big tensors with low multilinear rank. arxiv preprint arxiv:1412.1885 (2014). https://arxiv.org/pdf/1412.1885.pdf.Google Scholar
Index Terms
- A Distributed Tensor-Train Decomposition Method for Cyber-Physical-Social Services
Recommendations
Data fusion in cyber-physical-social systems: State-of-the-art and perspectives
Highlights- This paper analyzes data collection and representation in CPSS and proposes to use tensors to represent CPSS data.
AbstractCyber-Physical-Social systems (CPSSs) are the extension of Cyber-Physical systems (CPS), which seamlessly integrate cyber space, physical space and social space. CPSSs promote the information resource from single space to tri-space, so ...
A tensor-network-based big data fusion framework for Cyber–Physical–Social Systems (CPSS)
AbstractBig data has been continuously generated from the rapidly developing of cloud/fog/edge computing, Internet of Things (IoT) and 5G technology. This big data not only brings great benefits and opportunities to human beings but also brings many ...
Highlights- We review popular matrix decomposition, data fusion and tensor decomposition methods.
- A novel Tensor-Network-Conversion-based data fusion approach (TNC) is proposed in this paper.
- Our approach can simultaneously analyze multiple ...
A novel recursive least-squares adaptive method for streaming tensor-train decomposition with incomplete observations
AbstractTensor tracking which is referred to as online (adaptive) decomposition of streaming tensors has recently gained much attention in the signal processing community due to the fact that many modern applications generate a huge number of ...
Highlights- Low-rank approximation of streaming tensors is modeled under tensor-train format.
- Recursive least-squares and block coordinate descent methods are exploited.
- Our algorithm can handle incomplete streaming tensors in time-varying ...
Comments