Proceedings Article10.1109/COMPSAC.2019.00062
Multi-Source Heterogeneous Core Data Acquisition Method in Edge Computing Nodes
Hong Xia,Mingdao Zhao,Yanping Chen,Zhongmin Wang,Zhong Yu,Jingwei Yang +5 more
- 15 Jul 2019
- Vol. 1, pp 382-385
4
TL;DR: The experimental results show that the approximate tensor reconstructed from the tensor containing 15% of the core data can guarantee 90% accuracy and IncLHOSVD is significantly better than non-incremental HOSVD in execution time in guaranteeing the accuracy of approximate equal error.
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Abstract: As the volume of data grows exponentially, big data brings an unprecedented burden to the current computing infrastructure How to deal with big data efficiently and concisely and reduce the burden of computing infrastructure has always been a big challenge Therefore, this paper proposes a high-quality core data extraction method in edge computing nodes Firstly, heterogeneous data are fused into a unified model, the data characteristics of the original data are retained Then, a Lanzcosbased incremental tensor decomposition method is proposed to extracted the high quality core tensor dynamically Finally, the model algorithm is verified using real data The experimental results show that the approximate tensor reconstructed from the tensor containing 15% of the core data can guarantee 90% accuracy At the same time, IncLHOSVD is significantly better than non-incremental HOSVD in execution time in guaranteeing the accuracy of approximate equal error
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Citations
A Feature Fusion Analysis Model of Heterogeneous Data Based on Tensor Decomposition
Xianyang Chu,Minghua Zhu,Hongyan Mao,Yunzhou Qiu +3 more
- 18 Jul 2022
TL;DR: A feature fusion analysis model of heterogeneous data based on tensor decomposition that has an improvement of 18% compared with a single data source analysis model; compared with other heterogeneous fusion models, it can be increased by a maximum of 34%.
A Feature Fusion Analysis Model of Heterogeneous Data Based on Tensor Decomposition
18 Jul 2022
TL;DR: In this paper , a tensor autoencoder network based on subspace mapping is proposed to extract low-dimensional latent features from heterogeneous data, and then the Kronecker product operation is used to associate and fuse core data features from different subtensor spaces.
Traffic Flow Forecasting with Spatial-Temporal Graph Convolutional Networks in Edge-Computing Systems
Xianlu Xu,Haifeng Zheng,Xinxin Feng,Youjia Chen +3 more
- 21 Oct 2020
TL;DR: Wang et al. as discussed by the authors proposed a spatial-temporal graph convolutional network (STGCN-EC) for traffic flow prediction in an edge-computing system, where the road network is modeled as a graph and partition it into multiple subgraph according to the spatial correlation of the area so that traffic flow forecasting can be individually performed by each edge node.
Data Acquisition, Filtering and Buffering Protocol Design for Edge Computing Nodes
Xinyi Xu,Wenbin Dai +1 more
- 21 Jul 2021
TL;DR: In this paper, the data acquisition principle and process of the IEEE P2805.2 standard are explained in detail, and a case study is presented for proof-of-concept of the proposed protocol.
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