Smart city data analysis via visualization of correlated attribute patterns

Yuya Sasaki; Keizo Hori; Daiki Nishihara; Sora Ohashi; Yusuke Wakuta; Kei Harada; Makoto Onizuka; Yuki Arase; Shinji Shimojo; Kenji Doi; He Hongdi; Zhong Ren Peng

doi:10.5441/002/edbt.2021.75

Smart city data analysis via visualization of correlated attribute patterns

Yuya Sasaki
, Keizo Hori
, Daiki Nishihara
, Sora Ohashi
, Yusuke Wakuta
, Kei Harada
, Makoto Onizuka
, Yuki Arase
, Shinji Shimojo
, Kenji Doi
, He Hongdi
, Zhong Ren Peng

Osaka University
Shanghai Jiao Tong University
University of Florida

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Advances in Database Technology - EDBT 2021
Subtitle of host publication	24th International Conference on Extending Database Technology, Proceedings
Editors	Yannis Velegrakis, Yannis Velegrakis, Demetris Zeinalipour, Panos K. Chrysanthis, Panos K. Chrysanthis, Francesco Guerra
Publisher	OpenProceedings.org
Pages	650-653
Number of pages	4
ISBN (Electronic)	9783893180844
DOIs	https://doi.org/10.5441/002/edbt.2021.75
State	Published - 2021
Externally published	Yes
Event	Advances in Database Technology - 24th International Conference on Extending Database Technology, EDBT 2021 - Virtual, Online, Cyprus, Cyprus Duration: 23 Mar 2021 → 26 Mar 2021

Publication series

Name	Advances in Database Technology - EDBT
Volume	2021-March
ISSN (Electronic)	2367-2005

Conference

Conference	Advances in Database Technology - 24th International Conference on Extending Database Technology, EDBT 2021
Country/Territory	Cyprus
City	Virtual, Online, Cyprus
Period	23/03/21 → 26/03/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Access to Document

10.5441/002/edbt.2021.75

Cite this

Sasaki, Y., Hori, K., Nishihara, D., Ohashi, S., Wakuta, Y., Harada, K., Onizuka, M., Arase, Y., Shimojo, S., Doi, K., Hongdi, H., & Peng, Z. R. (2021). Smart city data analysis via visualization of correlated attribute patterns. In Y. Velegrakis, Y. Velegrakis, D. Zeinalipour, P. K. Chrysanthis, P. K. Chrysanthis, & F. Guerra (Eds.), Advances in Database Technology - EDBT 2021: 24th International Conference on Extending Database Technology, Proceedings (pp. 650-653). (Advances in Database Technology - EDBT; Vol. 2021-March). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2021.75

Sasaki, Yuya ; Hori, Keizo ; Nishihara, Daiki et al. / Smart city data analysis via visualization of correlated attribute patterns. Advances in Database Technology - EDBT 2021: 24th International Conference on Extending Database Technology, Proceedings. editor / Yannis Velegrakis ; Yannis Velegrakis ; Demetris Zeinalipour ; Panos K. Chrysanthis ; Panos K. Chrysanthis ; Francesco Guerra. OpenProceedings.org, 2021. pp. 650-653 (Advances in Database Technology - EDBT).

@inproceedings{2089a6223e6b42d69a4354c0a02a653a,

title = "Smart city data analysis via visualization of correlated attribute patterns",

abstract = "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 = "Yuya Sasaki and Keizo Hori and Daiki Nishihara and Sora Ohashi and Yusuke Wakuta and Kei Harada and Makoto Onizuka and Yuki Arase and Shinji Shimojo and Kenji Doi and He Hongdi and Peng, \{Zhong Ren\}",

note = "Publisher Copyright: {\textcopyright} 2021 Copyright held by the owner/author(s).; Advances in Database Technology - 24th International Conference on Extending Database Technology, EDBT 2021 ; Conference date: 23-03-2021 Through 26-03-2021",

year = "2021",

doi = "10.5441/002/edbt.2021.75",

language = "English",

series = "Advances in Database Technology - EDBT",

publisher = "OpenProceedings.org",

pages = "650--653",

editor = "Yannis Velegrakis and Yannis Velegrakis and Demetris Zeinalipour and Chrysanthis, \{Panos K.\} and Chrysanthis, \{Panos K.\} and Francesco Guerra",

booktitle = "Advances in Database Technology - EDBT 2021",

address = "Germany",

}

Sasaki, Y, Hori, K, Nishihara, D, Ohashi, S, Wakuta, Y, Harada, K, Onizuka, M, Arase, Y, Shimojo, S, Doi, K, Hongdi, H & Peng, ZR 2021, Smart city data analysis via visualization of correlated attribute patterns. in Y Velegrakis, Y Velegrakis, D Zeinalipour, PK Chrysanthis, PK Chrysanthis & F Guerra (eds), Advances in Database Technology - EDBT 2021: 24th International Conference on Extending Database Technology, Proceedings. Advances in Database Technology - EDBT, vol. 2021-March, OpenProceedings.org, pp. 650-653, Advances in Database Technology - 24th International Conference on Extending Database Technology, EDBT 2021, Virtual, Online, Cyprus, Cyprus, 23/03/21. https://doi.org/10.5441/002/edbt.2021.75

Smart city data analysis via visualization of correlated attribute patterns. / Sasaki, Yuya; Hori, Keizo; Nishihara, Daiki et al.
Advances in Database Technology - EDBT 2021: 24th International Conference on Extending Database Technology, Proceedings. ed. / Yannis Velegrakis; Yannis Velegrakis; Demetris Zeinalipour; Panos K. Chrysanthis; Panos K. Chrysanthis; Francesco Guerra. OpenProceedings.org, 2021. p. 650-653 (Advances in Database Technology - EDBT; Vol. 2021-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Smart city data analysis via visualization of correlated attribute patterns

AU - Sasaki, Yuya

AU - Hori, Keizo

AU - Nishihara, Daiki

AU - Ohashi, Sora

AU - Wakuta, Yusuke

AU - Harada, Kei

AU - Onizuka, Makoto

AU - Arase, Yuki

AU - Shimojo, Shinji

AU - Doi, Kenji

AU - Hongdi, He

AU - Peng, Zhong Ren

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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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M3 - Conference contribution

AN - SCOPUS:85113714014

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BT - Advances in Database Technology - EDBT 2021

A2 - Velegrakis, Yannis

A2 - Zeinalipour, Demetris

A2 - Chrysanthis, Panos K.

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T2 - Advances in Database Technology - 24th International Conference on Extending Database Technology, EDBT 2021

Y2 - 23 March 2021 through 26 March 2021

ER -

Sasaki Y, Hori K, Nishihara D, Ohashi S, Wakuta Y, Harada K et al. Smart city data analysis via visualization of correlated attribute patterns. In Velegrakis Y, Velegrakis Y, Zeinalipour D, Chrysanthis PK, Chrysanthis PK, Guerra F, editors, Advances in Database Technology - EDBT 2021: 24th International Conference on Extending Database Technology, Proceedings. OpenProceedings.org. 2021. p. 650-653. (Advances in Database Technology - EDBT). doi: 10.5441/002/edbt.2021.75

Smart city data analysis via visualization of correlated attribute patterns

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UN SDGs

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