Scalable Mining of Big Data
This paper focuses on scalable mining of huge volumes of temporal coronavirus disease 2019 (COVID-19) data at different granularity levels, and finds implicit, previously unknown and potentially useful information and knowledge which can be discovered by data mining for social good.
Abstract
Technological advancements have led to easy and rapid generation and collection of huge volumes of varieties of data from of wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by data mining. Discovered information and knowledge may help to build a smart world. In this paper, we present a solution for scalable mining of big data. In particular, we focus on scalable mining of huge volumes of temporal coronavirus disease 2019 (COVID-19) data at different granularity levels. Since its outbreak, there have been millions of COVID-19 cases worldwide. These are huge volumes of data, and new cases have been reported every day. Embedded in these COVID-19 data is implicit, previously unknown and potentially useful information and knowledge, which can be discovered by data mining for social good. Analyzing and mining these data helps users (e.g., researchers, civilian) to get better understanding of the disease, and thus take an active role in fighting, controlling, and/or combating the disease. Evaluation results on real-life COVID-19 data show the benefits of our solution in scalable mining of big data.