Data Mining for Visualizing Polluted Gases

Author:

Alsultanny Yas A.1ORCID

Affiliation:

1. Uruk University, Iraq

Abstract

Knowledge discovery from big data is one of the important issues. Big data mining needs many steps, which must be implemented carefully to get accurate results. Visualization is one of the 10 Vs characteristics of big data, and it is the final step in summarizing the results numerically. This article aims to mining the big data recorded by environmental station. These stations are recording the concentrations of pollution gases and meteorological parameters. The 2D and 3D data visualization are used to evaluate the capability of visualization in determining the effect of meteorological parameters on some gases that caused pollution. The results showed the visualization is a very important tool, and visualization can be used in mining big data by simply showing decision makers the pollution gases concentrations graphically. This article recommended using big data visualization periodically as an alarming tool with IoT for monitoring the levels of pollution gases concentration.

Publisher

IGI Global

Reference67 articles.

1. SAS.com/offices (2017). Data visualization techniques, from basics to big data with SAS-visual analytics, White Paper. https://www.sas.com/content/dam/SAS/documents/marketing-whitepapers-ebooks/sas-whitepapers/en/data-visualization-techniques-106006.pdf

2. Big data and disaster management: a systematic review and agenda for future research

3. Alsultanny, Y. (2011). Selecting a suitable method of data mining for successful forecasting. Journal of Targeting, Measurement and Analysis for Marketing, 19(3/4), 207–225. http://www.palgrave-journals.com/jt/journal/v19/n3/abs/jt201121a.html

4. Data mining and visualization: meteorological parameters and gas concentration use case.;Y.Alsultanny;Proceedings of the 19th International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID),2017

5. Alsultanny, Y. (2020). Machine learning by data mining REPTree and M5P for predicating novel information for PM10. Cloud Computing and Data Science, 1(1), 40-48. https://ojs.wiserpub.com/index.php/CCDS/article/view/418

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3