Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -