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Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors

文献类型：期刊论文

作者Luo, Xin3,4,5; Wu, Hao3,4; Yuan, Huaqiang5; Zhou, MengChu1,2
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2020-05-01
卷号50期号:5页码:1798-1809
关键词Quality of service Hidden Markov models Data models Training Web services Time factors Latent factor analysis (LFA) latent factorization of tensor learning temporal pattern linear bias (LB) non-negative latent factorization of tensor non-negativity constraint quality-of-service (QoS) prediction
ISSN号2168-2267
DOI10.1109/TCYB.2019.2903736
通讯作者Yuan, Huaqiang(yuanhq@dgut.edu.cn) ; Zhou, MengChu(zhou@njit.edu)
英文摘要Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly defined on static QoS data without the consideration of such temporal dynamics. To address this issue, this paper presents a biased non-negative latent factorization of tensors (BNLFTs) model for temporal pattern-aware QoS prediction. Its main idea is fourfold: 1) incorporating linear biases into the model for describing QoS fluctuations; 2) constraining the model to be non-negative for describing QoS non-negativity; 3) deducing a single LF-dependent, non-negative, and multiplicative update scheme for training the model; and 4) incorporating an alternating direction method into the model for faster convergence. The empirical studies on two dynamic QoS datasets from real applications show that compared with the state-of-the-art QoS-predictors, BNLFT represents temporal patterns more precisely with high computational efficiency, thereby achieving the most accurate predictions for missing QoS data.
资助项目National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61602352] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyfX0020] ; Chongqing Research Program of Technology Innovation and Application[cstc2017zdcy-zdyf0554] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyf0118] ; Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group[cstc2017kjrc-cxcytd0149] ; Chongqing Overseas Scholars Innovation Program[cx2017012] ; Chongqing Overseas Scholars Innovation Program[cx2018011] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:000528622000002
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/10901]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Yuan, Huaqiang; Zhou, MengChu
作者单位1.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
4.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
5.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Guangdong, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Xin,Wu, Hao,Yuan, Huaqiang,et al. Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(5):1798-1809.
APA Luo, Xin,Wu, Hao,Yuan, Huaqiang,&Zhou, MengChu.(2020).Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors.IEEE TRANSACTIONS ON CYBERNETICS,50(5),1798-1809.
MLA Luo, Xin,et al."Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors".IEEE TRANSACTIONS ON CYBERNETICS 50.5(2020):1798-1809.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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