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Choi, Jaesik
Statistical Artificial Intelligence Lab
Research Interests
  • Artificial intelligence, machine learning, deep learning, robotics, automatic statistician, semantic segmentation, fault detection

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A Scalable and Flexible Repository for Big Sensor Data

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Title
A Scalable and Flexible Repository for Big Sensor Data
Author
Lee, DongeunChoi, JaesikShin, Heonshik
Issue Date
2015-12
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE SENSORS JOURNAL, v.15, no.12, pp.7284 - 7294
Abstract
Data generation rates of sensors are rapidly increasing, reaching a limit such that storage expansion cannot keep up with the data growth. We propose a new big data archiving scheme that handles huge volume of sensor data with an optimized lossy coding. Our scheme leverages spatial and temporal correlations inherent in typical sensor data. The spatiotemporal correlations, observed in quality adjustable sensor data, enable us to compress a massive amount of sensor data without compromising distinctive attributes in sensor signals. Sensor data fidelity can also be decreased gradually. In order to maximize storage efficiency, we derive an optimal storage configuration for this data aging scenario. Experiments show outstanding compression ratios of our scheme and the optimality of storage configuration that minimizes system-wide distortion of sensor data under a given storage space.
URI
https://scholarworks.unist.ac.kr/handle/201301/17201
URL
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7217788
DOI
10.1109/JSEN.2015.2471802
ISSN
1530-437X
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EE_Journal Papers
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