Design and implementation of public data warehouse

Nurul Farhanah, Isha and Nor Azuana, Ramli and Qistina Batrisyia, Azman Shah (2024) Design and implementation of public data warehouse. In: AIP Conference Proceedings. 4th International Conference On Applied & Industrial Mathematics and Statistic 2023 (ICoAIMS 2023): Mathematics and Statistics for Technological Society , 22–24 August 2023 , Pahang, Malaysia. pp. 1-10., 3128 (030009). ISBN 978-0-7354-4957-2 (Published)

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Abstract

There is a large amount of public data available that is spread across various sources. However, these public data is spread across different government bodies’ websites and repository platforms. Data must be collected and stored in a warehouse for utilization. Hence, this research is conducted to design and implement an appropriate data warehouse that is able to merge all public data from different government bodies and public websites in one data warehouse. This research started by locating relevant sources from various open data platforms such as Public Info Banjir and the Official Portal iDengue for community version 3.0. Then, significant data were collected such as river water level data, rainfall data, dengue locality cases and weather data. A public data warehouse was constructed using the bottom-up approach of the Kimball methodology. Following this, a galaxy schema model was identified to design the fact and dimension tables, which is then converted into a logical model. Finally, the structured data warehouse was developed. The public data warehouse, containing data such as water level, rainfall, dengue locality cases and weather data will be of great benefit to both government bodies and the public. For future study, the data warehouse enables the development of advanced analytics and predictive models, which helps to enhance decision-making and optimize infrastructure management in relation to flood mitigation efforts.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Data Warehouse; Decision-Making; Business Intelligence
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Center for Mathematical Science Institute of Postgraduate Studies
Depositing User:	Dr. Nor Azuana Ramli
Date Deposited:	06 Sep 2024 07:30
Last Modified:	06 Sep 2024 07:30
URI:	http://umpir.ump.edu.my/id/eprint/42515
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