A multidimensional data warehouse design to combat the health pandemics

Abstract

The Covid-19 pandemic has brought about a new lifestyle for across the globe. Throughout this period, the use of holistic methods has become indispensable to deal with the enormous amount of data in this regard. It appears that the simplest way to tackle this issue is to spread the digitalization efforts concerning all data-based applications. Given the significance of pandemic data management, it is essential to have a data warehouse that collects, associates, and communicates these data. Containing a significant volume of structured data, warehousing can provide the necessary foundation for data mining and the development of analytical tools. To this end, the present paper proposes a data warehouse for combatting and managing pandemics, with the possibility to be enhanced for other personal or public health-related initiatives. In this research, the bottom-up data warehouse building methodology is used to construct a warehouse. A fact constellation schema model is utilized to accommodate the information ranging from citizen demographics to physician-prescribed drugs and laboratory tests. Sample queries are executed based on the proposed data warehouse for different purposes, and desired query results are obtained within proper response times. The proposed data warehouse contributes to countrywide implementation of pandemic practices and illuminates research on faster, less expensive, and safer management of citywide, nationwide, or worldwide health emergencies within a robust technical framework by governments.

Dimensional Data Design for Event Feedback Data Warehouse

“…The Kimball method takes a bottom-up approach that focuses on answering business questions of a specific domain. The method resulted in several tables called 'fact' and 'dimension' (Turcan & Peker, 2022). The dimension table represents an entity along with its attributes.…”

“…Entities are represented in dimension tables to support the filtering and grouping of facts (Lapura et al, 2018). The identified dimensions are obtained based on the subject of the business process (Turcan & Peker, 2022). Dimension tables are commonly created in a data warehouse including time and date dimensions (Yulianto, 2019).…”