Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 1/04/2017 |
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Host publication | Database Systems for Advanced Applications |
Editors | S. Candan, L. Chen, T. Pedersen, L. Chang, W. Hua |
Place of Publication | Cham |
Publisher | Springer |
Pages | 607-622 |
Number of pages | 16 |
ISBN (electronic) | 9783319557533 |
ISBN (print) | 9783319557526 |
<mark>Original language</mark> | English |
Event | 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017 - Suzhou, China Duration: 27/03/2017 → 30/03/2017 |
Conference | 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017 |
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Country/Territory | China |
City | Suzhou |
Period | 27/03/17 → 30/03/17 |
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Publisher | Springer |
Volume | 10177 |
ISSN (Print) | 0302-9743 |
ISSN (electronic) | 1611-3349 |
Conference | 22nd International Conference on Database Systems for Advanced Applications, DASFAA 2017 |
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Country/Territory | China |
City | Suzhou |
Period | 27/03/17 → 30/03/17 |
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.