The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

With the growing attention to evidence-based medical guideline development, longitudinal analysis of Electronic Medical Records (EMR) has become a good tool for providing insight into and new knowledge on the existing therapy. For chronic diseases, longitudinal analysis of medication history plays a key role in reaching this goal. However, raw medication data in EMR are not suitable for longitudinal analysis for several reasons. First, many prescriptions have a short duration. Second, the prescription duration may have a gap or overlap with other prescription durations. Additionally, for diabetes cases, physicians must wait for a certain period to observe the effectiveness of the medication. However, the existing methods do not address these conditions. To tackle these issues, we propose a set of rules for medication episode reconstruction. We then apply the rules for longitudinal analysis on anonymous Type 2 diabetes patients' EMR provided by Kyoto University Hospital. The EMR span from 2000 to 2015. Two of our significant results are as follows: (1) our proposed medication episode reconstruction method is able to compress the search space into 23.83% compared to the raw data, and (2) the preliminary results show the benefits of the method in revealing the existing medication patterns over the years and unfamiliar therapy transition.

Our proposed framework is designed for multitherapy datasets, which has not been addressed by previous studies. The concept of relaxes the prescription relation against noise caused by the patient behavior and consequently provides a compact, but informative search space for observing medication transition events in a longitudinal analysis.

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