Smart City Data Analysis via Visualization of Correlated Attribute Patterns
EDBT 2021 Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlati...
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Main Authors | , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
14.04.2021
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Abstract | EDBT 2021 Urban conditions are monitored by a wide variety of sensors that measure
several attributes, such as temperature and traffic volume. The correlations of
sensors help to analyze and understand the urban conditions accurately. The
correlated attribute pattern (CAP) mining discovers correlations among multiple
attributes from the sets of sensors spatially close to each other and
temporally correlated in their measurements. In this paper, we develop a
visualization system for CAP mining and demonstrate analysis of smart city
data. Our visualization system supports an intuitive understanding of mining
results via sensor locations on maps and temporal changes of their
measurements. In our demonstration scenarios, we provide four smart city
datasets collected from China and Santander, Spain. We demonstrate that our
system helps interactive analysis of smart city data. |
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AbstractList | EDBT 2021 Urban conditions are monitored by a wide variety of sensors that measure
several attributes, such as temperature and traffic volume. The correlations of
sensors help to analyze and understand the urban conditions accurately. The
correlated attribute pattern (CAP) mining discovers correlations among multiple
attributes from the sets of sensors spatially close to each other and
temporally correlated in their measurements. In this paper, we develop a
visualization system for CAP mining and demonstrate analysis of smart city
data. Our visualization system supports an intuitive understanding of mining
results via sensor locations on maps and temporal changes of their
measurements. In our demonstration scenarios, we provide four smart city
datasets collected from China and Santander, Spain. We demonstrate that our
system helps interactive analysis of smart city data. |
Author | Hongdi, He Arase, Yuki Harada, Kei Doi, Kenji Sasaki, Yuya Onizuka, Makoto Wakuta, Yusuke Shimojo, Shinji Hori, Keizo Peng, Zhong-Ren Nishihara, Daiki Ohashi, Sora |
Author_xml | – sequence: 1 givenname: Yuya surname: Sasaki fullname: Sasaki, Yuya – sequence: 2 givenname: Keizo surname: Hori fullname: Hori, Keizo – sequence: 3 givenname: Daiki surname: Nishihara fullname: Nishihara, Daiki – sequence: 4 givenname: Sora surname: Ohashi fullname: Ohashi, Sora – sequence: 5 givenname: Yusuke surname: Wakuta fullname: Wakuta, Yusuke – sequence: 6 givenname: Kei surname: Harada fullname: Harada, Kei – sequence: 7 givenname: Makoto surname: Onizuka fullname: Onizuka, Makoto – sequence: 8 givenname: Yuki surname: Arase fullname: Arase, Yuki – sequence: 9 givenname: Shinji surname: Shimojo fullname: Shimojo, Shinji – sequence: 10 givenname: Kenji surname: Doi fullname: Doi, Kenji – sequence: 11 givenname: He surname: Hongdi fullname: Hongdi, He – sequence: 12 givenname: Zhong-Ren surname: Peng fullname: Peng, Zhong-Ren |
BackLink | https://doi.org/10.48550/arXiv.2104.06701$$DView paper in arXiv |
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Snippet | EDBT 2021 Urban conditions are monitored by a wide variety of sensors that measure
several attributes, such as temperature and traffic volume. The correlations... |
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Title | Smart City Data Analysis via Visualization of Correlated Attribute Patterns |
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