TY - JOUR
T1 - Tensor-Train-Based High-Order Dominant Eigen Decomposition for Multimodal Prediction Services
AU - Liu, Huazhong
AU - Yang, Laurence Tianruo
AU - Ding, Jihong
AU - Guo, Yimu
AU - Yau, Stephen S.
N1 - Funding Information:
Manuscript received July 28, 2018; revised January 31, 2019; accepted March 21, 2019. Date of publication July 2, 2019; date of current version November 13, 2020. This work was supported in part by the National Key R&D Program of China under Grant 2017YFB0801804, in part by the National Natural Science Foundation of China under Grant 61867002 and Grant 71704160, in part by the Fundamental Research Funds for the Central Universities under Grant 2018KFYXKJC046, and in part by the Shenzhen Fundamental Research Program under Grant JCYJ20170307172200714. Review of this manuscript was arranged by Department Editor P. Hung. (Corresponding author: Jihong Ding.) H. Liu is with the School of Computer Science and Technology and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China, with Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518000, China, and also with the School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China (e-mail:,sharpshark_ding@163.com).
Funding Information:
This work was supported in part by the National Key RandD Program of China under Grant 2017YFB0801804, in part by the National Natural Science Foundation of China under Grant 61867002 and Grant 71704160, in part by the Fundamental Research Funds for the Central Universities under Grant 2018KFYXKJC046, and in part by the Shenzhen Fundamental Research Program under Grant JCYJ20170307172200714.
Publisher Copyright:
© 1988-2012 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - By leveraging neoteric analytical techniques associated with big data, numerous new data-focused computation and service models have flourished in service computing systems. Accurate future predictions based on tensor-based multivariate Markov models can vigorously support enterprise decisions. However, the computation efficiency and quick response of tensor-based multimodal prediction approach are seriously restricted by the curse of dimensionality arising from high-order tensor. Therefore, to alleviate the problem, this paper focuses on proposing a tensor-train (TT)-based computation approach with its scalable implementation for high-order dominant eigen decomposition (HODED) in multivariate Markov models. First, we present a TT-based Einstein product directly based on decomposed TT cores and guarantee that the result remains TT format. Then, we put forward a scalable implementation for TT-based Einstein product in a distributed or parallel manner. Afterwards, we propose a scalable TT-based HODED (TT-HODED) algorithm and a multimodal accurate prediction algorithm. Furthermore, a TT-based big data processing and services framework is presented to provide accurate proactive services. Experimental results based on real-world GPS trajectory dataset demonstrate that TT-HODED algorithm can significantly improve the computation efficiency and reduce the running memory on the premise of guaranteeing the almost consistent prediction accuracy compared to the original HODED algorithm.
AB - By leveraging neoteric analytical techniques associated with big data, numerous new data-focused computation and service models have flourished in service computing systems. Accurate future predictions based on tensor-based multivariate Markov models can vigorously support enterprise decisions. However, the computation efficiency and quick response of tensor-based multimodal prediction approach are seriously restricted by the curse of dimensionality arising from high-order tensor. Therefore, to alleviate the problem, this paper focuses on proposing a tensor-train (TT)-based computation approach with its scalable implementation for high-order dominant eigen decomposition (HODED) in multivariate Markov models. First, we present a TT-based Einstein product directly based on decomposed TT cores and guarantee that the result remains TT format. Then, we put forward a scalable implementation for TT-based Einstein product in a distributed or parallel manner. Afterwards, we propose a scalable TT-based HODED (TT-HODED) algorithm and a multimodal accurate prediction algorithm. Furthermore, a TT-based big data processing and services framework is presented to provide accurate proactive services. Experimental results based on real-world GPS trajectory dataset demonstrate that TT-HODED algorithm can significantly improve the computation efficiency and reduce the running memory on the premise of guaranteeing the almost consistent prediction accuracy compared to the original HODED algorithm.
KW - Accurate services
KW - big data
KW - high-order dominant eigen decomposition (HODED)
KW - multimodal prediction
KW - multivariate Markov model
KW - scalable tensor computations
KW - tensor-train (TT)-based Einstein product
UR - http://www.scopus.com/inward/record.url?scp=85076789970&partnerID=8YFLogxK
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U2 - 10.1109/TEM.2019.2912928
DO - 10.1109/TEM.2019.2912928
M3 - Article
AN - SCOPUS:85076789970
SN - 0018-9391
VL - 68
SP - 197
EP - 211
JO - IEEE Transactions on Engineering Management
JF - IEEE Transactions on Engineering Management
IS - 1
M1 - 8753660
ER -