© 2021, Springer Nature Singapore Pte Ltd. A smart world can be considered as a convergence of the physical world, cyber world, social world, and thinking world. In these four worlds, huge amounts of valuable data are generated and gathered at a rapid rate from a broad range of data sources. Although the quality of these big data depend on their degrees of uncertainty, rich sets of valuable information and useful knowledge can be mined from the big data. This paper focuses on big data computing and mining, which aims to (a) analyze these rich sets of big data, and (b) discover implicit, previously unknown, and potentially useful information and knowledge from the big data. In particular, we present data science solutions for discover frequent patterns. Through our presentation, we discuss how these solutions interconnecting (a) big data generated and collected from the physical world, (b) frequent pattern mining algorithms in the cyber world, (c) social interactions among social individuals in the social world, and (d) user preference and interest reflecting the user cognitive thinking in the thinking world. We show these interactions through our discussion on mining coronavirus disease 2019 (COVID-19) data in a smart world environment. The interconnections link the physical, cyber, social and thinking worlds together to establish a better environment towards big data computing and mining in a smart world.