Big data framework to evaluate and analyse National Covid-19 Immunization Programmed (NCIP) in Malaysia: a comparative study - Muhamad Ibrahim, Najhan; Ismail, Amelia Ritahani (Taylor’s University, 2023)
Many governments around the world have launched their open government data (OGD) portal to improve the government’s transparency by sharing their data with the public such as National Covid-19 Immunization Pro-grammed (NCIP), which has been published at https://github.com/CITF-Malaysia/citf-public. However, increasing the number of datasets, data types, volume and complexity will be raised the integration issues. There-fore, it is essential to evaluate and analyses those huge amounts of these da-tasets. NCIP provides multiple data sources and datasets. These may raise the Big Data (BD) issues and pose various evaluation and analysis prob-lems to produce valuable information. To generate meaningful linked data to support the purposes of this research study, the relationship between these disparate datasets needs to be identified and construct a comprehen-sive framework. In order to understand the causes of OGD development of big data, this study involves a detailed examination and comparison of ex-isting theories and actual approaches to handle public sector open data con-cerns. According to the review, the framework was dominantly adopted over architecture, infrastructures, theoretical and conceptual framework in previous research to examine the revolution of government public accessi-ble data. According to the findings, most existing frameworks do not con-sider the demand for public open data in health such as NCPI. Previous re-search on OGD for health has a lesser number of advanced BD frameworks. In the public sector, there is still a lack of investment and use of Big Data. The findings will aid academics in doing empirical research on the revealed need, as well as offer decision-makers with a better understanding of how to leverage OGD adoption in health by taking relevant actions.