Information extraction methodology by web scraping for smart cities

Using machine learning to train air quality monitor for smart cities

Chia Chun Chung, Taysheng Jeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an opportunistic sensing system for air quality monitoring to forecast the implicit factors of air pollution. Opportunistic sensing is performed by web scraping in the social network service to extract information. The data source for the air quality analysis combines two types of information: explicit and implicit information. The objective is to develop the information extraction methodology by web scraping for smart cities. The application development methodology has potential for solving real-world problems such as air pollution by data comparison between social activity observing and data collecting in sensor network.

Original languageEnglish
Title of host publicationCAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia
Subtitle of host publicationLearning, Prototyping and Adapting
EditorsSuleiman Alhadidi, Tomohiro Fukuda, Weixin Huang, Patrick Janssen, Kristof Crolla
PublisherThe Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
Pages515-524
Number of pages10
Volume2
ISBN (Electronic)9789887891703
Publication statusPublished - 2018 Jan 1
Event23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018 - Beijing, China
Duration: 2018 May 172018 May 19

Other

Other23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018
CountryChina
CityBeijing
Period18-05-1718-05-19

Fingerprint

Air pollution
Air quality
Learning systems
Sensor networks
Monitoring
Smart city

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Building and Construction

Cite this

Chung, C. C., & Jeng, T. (2018). Information extraction methodology by web scraping for smart cities: Using machine learning to train air quality monitor for smart cities. In S. Alhadidi, T. Fukuda, W. Huang, P. Janssen, & K. Crolla (Eds.), CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting (Vol. 2, pp. 515-524). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA).
Chung, Chia Chun ; Jeng, Taysheng. / Information extraction methodology by web scraping for smart cities : Using machine learning to train air quality monitor for smart cities. CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting. editor / Suleiman Alhadidi ; Tomohiro Fukuda ; Weixin Huang ; Patrick Janssen ; Kristof Crolla. Vol. 2 The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2018. pp. 515-524
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title = "Information extraction methodology by web scraping for smart cities: Using machine learning to train air quality monitor for smart cities",
abstract = "This paper presents an opportunistic sensing system for air quality monitoring to forecast the implicit factors of air pollution. Opportunistic sensing is performed by web scraping in the social network service to extract information. The data source for the air quality analysis combines two types of information: explicit and implicit information. The objective is to develop the information extraction methodology by web scraping for smart cities. The application development methodology has potential for solving real-world problems such as air pollution by data comparison between social activity observing and data collecting in sensor network.",
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Chung, CC & Jeng, T 2018, Information extraction methodology by web scraping for smart cities: Using machine learning to train air quality monitor for smart cities. in S Alhadidi, T Fukuda, W Huang, P Janssen & K Crolla (eds), CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting. vol. 2, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), pp. 515-524, 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA 2018, Beijing, China, 18-05-17.

Information extraction methodology by web scraping for smart cities : Using machine learning to train air quality monitor for smart cities. / Chung, Chia Chun; Jeng, Taysheng.

CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting. ed. / Suleiman Alhadidi; Tomohiro Fukuda; Weixin Huang; Patrick Janssen; Kristof Crolla. Vol. 2 The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2018. p. 515-524.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - This paper presents an opportunistic sensing system for air quality monitoring to forecast the implicit factors of air pollution. Opportunistic sensing is performed by web scraping in the social network service to extract information. The data source for the air quality analysis combines two types of information: explicit and implicit information. The objective is to develop the information extraction methodology by web scraping for smart cities. The application development methodology has potential for solving real-world problems such as air pollution by data comparison between social activity observing and data collecting in sensor network.

AB - This paper presents an opportunistic sensing system for air quality monitoring to forecast the implicit factors of air pollution. Opportunistic sensing is performed by web scraping in the social network service to extract information. The data source for the air quality analysis combines two types of information: explicit and implicit information. The objective is to develop the information extraction methodology by web scraping for smart cities. The application development methodology has potential for solving real-world problems such as air pollution by data comparison between social activity observing and data collecting in sensor network.

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M3 - Conference contribution

VL - 2

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EP - 524

BT - CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia

A2 - Alhadidi, Suleiman

A2 - Fukuda, Tomohiro

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A2 - Janssen, Patrick

A2 - Crolla, Kristof

PB - The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)

ER -

Chung CC, Jeng T. Information extraction methodology by web scraping for smart cities: Using machine learning to train air quality monitor for smart cities. In Alhadidi S, Fukuda T, Huang W, Janssen P, Crolla K, editors, CAADRIA 2018 - 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting. Vol. 2. The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). 2018. p. 515-524