| Title | Visualization Of Big Data As Urban Drought Monitoring Support In Smart Cities |
|---|---|
| ID_Doc | 41802 |
| Authors | Tihi, N; Popov, S; Bondzic, J; Dujovic, M |
| Title | Visualization Of Big Data As Urban Drought Monitoring Support In Smart Cities |
| Year | 2021 |
| Published | Fresenius Environmental Bulletin, 30, 1 |
| Abstract | In the last few decades, urbanization and rapid population growth have led to various social, economic, technical, and organizational problems in big cities. The Smart City concept, which has emerged as a solution, aims to integrate various technologies such as IoT (Internet of Things) and Big Data Analytics to enable the user to obtain useful real-time information. One of the most important products of Data Analytics is Data mining which represents a process of analyzing data and finding hidden patterns of behavior in data using automatic or semi-automatic methods. Data mining has also found its application in the concept of Smart Cities where it is used for analyzing Big Data. Data preparation and visualization is one of the essential tasks of Data mining. This paper aims to present a methodology on how to successfully process and visualize collected IoT Big Data in a selected programming language and environment called R for water shortage observation purpose. This research represents a base for further work which will be focused on the selection of certain machine learning algorithms for further data analysis. Obtained results could be used for various purposes of improving the quality of life in smart cities such as urban drought forecasting. |
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