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CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications

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

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Standard

CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications. / Kawano, M.; Yonezawa, T.; Tanimura, T. et al.
Urb-IoT 2018: 3rd EAI International Conference on IoT in Urban Space . ed. / R. José ; K. Van Laerhoven; H. Rodrgues. Cham: Springer Science and Business Media Deutschland GmbH, 2019. (EAI/Springer Innovations in Communication and Computing).

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

Harvard

Kawano, M, Yonezawa, T, Tanimura, T, Giang, NK, Broadbent, M, Lea, R & Nakazawa, J 2019, CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications. in R José , K Van Laerhoven & H Rodrgues (eds), Urb-IoT 2018: 3rd EAI International Conference on IoT in Urban Space . EAI/Springer Innovations in Communication and Computing, Springer Science and Business Media Deutschland GmbH, Cham. https://doi.org/10.1007/978-3-030-28925-6_1

APA

Kawano, M., Yonezawa, T., Tanimura, T., Giang, N. K., Broadbent, M., Lea, R., & Nakazawa, J. (2019). CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications. In R. José , K. Van Laerhoven, & H. Rodrgues (Eds.), Urb-IoT 2018: 3rd EAI International Conference on IoT in Urban Space (EAI/Springer Innovations in Communication and Computing). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-28925-6_1

Vancouver

Kawano M, Yonezawa T, Tanimura T, Giang NK, Broadbent M, Lea R et al. CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications. In José R, Van Laerhoven K, Rodrgues H, editors, Urb-IoT 2018: 3rd EAI International Conference on IoT in Urban Space . Cham: Springer Science and Business Media Deutschland GmbH. 2019. (EAI/Springer Innovations in Communication and Computing). doi: 10.1007/978-3-030-28925-6_1

Author

Kawano, M. ; Yonezawa, T. ; Tanimura, T. et al. / CityFlow : Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications. Urb-IoT 2018: 3rd EAI International Conference on IoT in Urban Space . editor / R. José ; K. Van Laerhoven ; H. Rodrgues. Cham : Springer Science and Business Media Deutschland GmbH, 2019. (EAI/Springer Innovations in Communication and Computing).

Bibtex

@inproceedings{3d30dd943bcd44fcbad5c5286d0ae848,
title = "CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications",
abstract = "A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities. {\textcopyright} 2020, Springer Nature Switzerland AG.",
keywords = "Edge processing, Participatory sensing, Road damage detection, Smart city, Urban computing, Damage detection, Data flow analysis, Edge computing, Automatic labeling, Data flow modeling, Integrated development environment, Machine learning applications, Machine learning techniques, Participatory sensing applications, Spatial and temporal variances, Transport infrastructure, Machine learning",
author = "M. Kawano and T. Yonezawa and T. Tanimura and N.K. Giang and M. Broadbent and R. Lea and J. Nakazawa",
year = "2019",
month = nov,
day = "14",
doi = "10.1007/978-3-030-28925-6_1",
language = "English",
isbn = "9783030289249",
series = "EAI/Springer Innovations in Communication and Computing",
publisher = "Springer Science and Business Media Deutschland GmbH",
editor = "{Jos{\'e} }, R. and {Van Laerhoven}, K. and H. Rodrgues",
booktitle = "Urb-IoT 2018",
address = "Germany",

}

RIS

TY - GEN

T1 - CityFlow

T2 - Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications

AU - Kawano, M.

AU - Yonezawa, T.

AU - Tanimura, T.

AU - Giang, N.K.

AU - Broadbent, M.

AU - Lea, R.

AU - Nakazawa, J.

PY - 2019/11/14

Y1 - 2019/11/14

N2 - A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities. © 2020, Springer Nature Switzerland AG.

AB - A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities. © 2020, Springer Nature Switzerland AG.

KW - Edge processing

KW - Participatory sensing

KW - Road damage detection

KW - Smart city

KW - Urban computing

KW - Damage detection

KW - Data flow analysis

KW - Edge computing

KW - Automatic labeling

KW - Data flow modeling

KW - Integrated development environment

KW - Machine learning applications

KW - Machine learning techniques

KW - Participatory sensing applications

KW - Spatial and temporal variances

KW - Transport infrastructure

KW - Machine learning

U2 - 10.1007/978-3-030-28925-6_1

DO - 10.1007/978-3-030-28925-6_1

M3 - Conference contribution/Paper

SN - 9783030289249

T3 - EAI/Springer Innovations in Communication and Computing

BT - Urb-IoT 2018

A2 - José , R.

A2 - Van Laerhoven, K.

A2 - Rodrgues, H.

PB - Springer Science and Business Media Deutschland GmbH

CY - Cham

ER -