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Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion

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Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion. / Ruan, Wenjie; Xu, Peipei; Sheng, Quan Z.; Falkner, Nickolas J.G.; Li, Xue; Zhang, Wei Emma.

Database Systems for Advanced Applications . ed. / S. Candan; L. Chen; T. Pedersen; L. Chang; W. Hua. Cham : Springer, 2017. p. 607-622 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10177).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Ruan, W, Xu, P, Sheng, QZ, Falkner, NJG, Li, X & Zhang, WE 2017, Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion. in S Candan, L Chen, T Pedersen, L Chang & W Hua (eds), Database Systems for Advanced Applications . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10177, Springer, Cham, pp. 607-622, 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017, Suzhou, China, 27/03/17. https://doi.org/10.1007/978-3-319-55753-3_38

APA

Ruan, W., Xu, P., Sheng, Q. Z., Falkner, N. J. G., Li, X., & Zhang, W. E. (2017). Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion. In S. Candan, L. Chen, T. Pedersen, L. Chang, & W. Hua (Eds.), Database Systems for Advanced Applications (pp. 607-622). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10177). Springer. https://doi.org/10.1007/978-3-319-55753-3_38

Vancouver

Ruan W, Xu P, Sheng QZ, Falkner NJG, Li X, Zhang WE. Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion. In Candan S, Chen L, Pedersen T, Chang L, Hua W, editors, Database Systems for Advanced Applications . Cham: Springer. 2017. p. 607-622. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-55753-3_38

Author

Ruan, Wenjie ; Xu, Peipei ; Sheng, Quan Z. ; Falkner, Nickolas J.G. ; Li, Xue ; Zhang, Wei Emma. / Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion. Database Systems for Advanced Applications . editor / S. Candan ; L. Chen ; T. Pedersen ; L. Chang ; W. Hua. Cham : Springer, 2017. pp. 607-622 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{3882bd311688448089be683e63745056,
title = "Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion",
abstract = "With the booming of the Internet of Things, tremendous amount of sensors have been installed in different geographic locations, generating massive sensory data with both time-stamps and geo-tags. Such type of data usually have shown complex spatio-temporal correlation and are easily missing in practice due to communication failure or data corruption. In this paper, we aim to tackle the challenge-how to accurately and efficiently recover the missing values for corrupted spatiotemporal sensory data. Specifically, we first formulate such sensor data as a high-dimensional tensor that can naturally preserve sensors{\textquoteright} both geographical and time information, thus we call spatio-temporal Tensor. Then we model the sensor data recovery as a low-rank robust tensor completion problem by exploiting its latent low-rank structure and sparse noise property. To solve this optimization problem, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to minimize the tensor{\textquoteright}s convex surrogate and noise{\textquoteright}s ℓ1-norm. In addition to testing our method by a synthetic dataset, we also use passive RFID (radiofrequency identification) sensors to build a real-world sensor-array testbed, which generates overall 115,200 sensor readings for model evaluation. The experimental results demonstrate the accuracy and robustness of our approach.",
author = "Wenjie Ruan and Peipei Xu and Sheng, {Quan Z.} and Falkner, {Nickolas J.G.} and Xue Li and Zhang, {Wei Emma}",
year = "2017",
month = apr,
day = "1",
doi = "10.1007/978-3-319-55753-3_38",
language = "English",
isbn = "9783319557526",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "607--622",
editor = "S. Candan and L. Chen and T. Pedersen and L. Chang and W. Hua",
booktitle = "Database Systems for Advanced Applications",
note = "22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017 ; Conference date: 27-03-2017 Through 30-03-2017",

}

RIS

TY - GEN

T1 - Recovering missing values from corrupted spatio-temporal sensory data via robust low-rank tensor completion

AU - Ruan, Wenjie

AU - Xu, Peipei

AU - Sheng, Quan Z.

AU - Falkner, Nickolas J.G.

AU - Li, Xue

AU - Zhang, Wei Emma

PY - 2017/4/1

Y1 - 2017/4/1

N2 - With the booming of the Internet of Things, tremendous amount of sensors have been installed in different geographic locations, generating massive sensory data with both time-stamps and geo-tags. Such type of data usually have shown complex spatio-temporal correlation and are easily missing in practice due to communication failure or data corruption. In this paper, we aim to tackle the challenge-how to accurately and efficiently recover the missing values for corrupted spatiotemporal sensory data. Specifically, we first formulate such sensor data as a high-dimensional tensor that can naturally preserve sensors’ both geographical and time information, thus we call spatio-temporal Tensor. Then we model the sensor data recovery as a low-rank robust tensor completion problem by exploiting its latent low-rank structure and sparse noise property. To solve this optimization problem, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to minimize the tensor’s convex surrogate and noise’s ℓ1-norm. In addition to testing our method by a synthetic dataset, we also use passive RFID (radiofrequency identification) sensors to build a real-world sensor-array testbed, which generates overall 115,200 sensor readings for model evaluation. The experimental results demonstrate the accuracy and robustness of our approach.

AB - With the booming of the Internet of Things, tremendous amount of sensors have been installed in different geographic locations, generating massive sensory data with both time-stamps and geo-tags. Such type of data usually have shown complex spatio-temporal correlation and are easily missing in practice due to communication failure or data corruption. In this paper, we aim to tackle the challenge-how to accurately and efficiently recover the missing values for corrupted spatiotemporal sensory data. Specifically, we first formulate such sensor data as a high-dimensional tensor that can naturally preserve sensors’ both geographical and time information, thus we call spatio-temporal Tensor. Then we model the sensor data recovery as a low-rank robust tensor completion problem by exploiting its latent low-rank structure and sparse noise property. To solve this optimization problem, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to minimize the tensor’s convex surrogate and noise’s ℓ1-norm. In addition to testing our method by a synthetic dataset, we also use passive RFID (radiofrequency identification) sensors to build a real-world sensor-array testbed, which generates overall 115,200 sensor readings for model evaluation. The experimental results demonstrate the accuracy and robustness of our approach.

U2 - 10.1007/978-3-319-55753-3_38

DO - 10.1007/978-3-319-55753-3_38

M3 - Conference contribution/Paper

AN - SCOPUS:85032300663

SN - 9783319557526

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 607

EP - 622

BT - Database Systems for Advanced Applications

A2 - Candan, S.

A2 - Chen, L.

A2 - Pedersen, T.

A2 - Chang, L.

A2 - Hua, W.

PB - Springer

CY - Cham

T2 - 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017

Y2 - 27 March 2017 through 30 March 2017

ER -