Big-data Analytics: Exploring the Well-being Trend in South Korea Through Inductive Reasoning

• Younghan Lee,
• Mi-Lyang Kim,
• Seoyoun Hong,

Vol. 15, No. 6, June 30, 2021
10.3837/tiis.2021.06.003, Download Paper (Free):

• Apriori Algorithm
• Big-data analytics
• Degree of visibility
• Inductive reasoning
• Keyword emergence map
• Well-being

Abstract

To understand a trend is to explore the intricate process of how something or a particular situation is constantly changing or developing in a certain direction. This exploration is about observing and describing an unknown field of knowledge, not testing theories or models with a preconceived hypothesis. The purpose is to gain knowledge we did not expect and to recognize the associations among the elements that were suspected or not. This generally requires examining a massive amount of data to find information that could be transformed into meaningful knowledge. That is, looking through the lens of big-data analytics with an inductive reasoning approach will help expand our understanding of the complex nature of a trend. The current study explored the trend of well-being in South Korea using big-data analytic techniques to discover hidden search patterns, associative rules, and keyword signals. Thereafter, a theory was developed based on inductive reasoning – namely the hook, upward push, and downward pull to elucidate a holistic picture of how big-data implications alongside social phenomena may have influenced the well-being trend.

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