Scalable Mining of Big Data

被引:4
|
作者
Leung, Carson K. [1 ]
Pazdor, Adam G. M. [1 ]
Zheng, Hao [1 ]
机构
[1] Univ Manitoba, Dept Comp Sci, Winnipeg, MB, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Smart world; scalable computing; big data; data science; data mining; data analytics; health analytics; health informatics; coronavirus disease; COVID-19; temporal data; COVID-19;
D O I
10.1109/SWC50871.2021.00041
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:240 / 247
页数:8
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