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

Makoto Kawano, Takuro Yonezawa, Tomoki Tanimura, Nam Ky Giang, Matthew Broadbent, Rodger Lea, Jin Nakazawa

研究成果: Chapter

抄録

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.

本文言語English
ホスト出版物のタイトルEAI/Springer Innovations in Communication and Computing
出版社Springer Science and Business Media Deutschland GmbH
ページ3-15
ページ数13
DOI
出版ステータスPublished - 2020

出版物シリーズ

名前EAI/Springer Innovations in Communication and Computing
ISSN(印刷版)2522-8595
ISSN(電子版)2522-8609

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 電子工学および電気工学
  • 健康情報学

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