| id |
caadria2018_085 |
| authors |
Chung, Chia-Chun and Jeng, Tay-Sheng |
| year |
2018 |
| title |
Information Extraction Methodology by Web Scraping for Smart Cities - Using Machine Learning to Train Air Quality Monitor for Smart Cities |
| source |
T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 515-524 |
| doi |
https://doi.org/10.52842/conf.caadria.2018.2.515
|
| summary |
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. |
| keywords |
smart city; open data; web scraping; social media; machine learning |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,930,221 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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