TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Big-data Analytics: Exploring the Well-being Trend in South Korea Through Inductive Reasoning |
| 영문제목(English Title) |
Big-data Analytics: Exploring the Well-being Trend in South Korea Through Inductive Reasoning |
| 저자(Author) |
Younghan Lee
Mi-Lyang Kim
Seoyoun Hong
|
| 원문수록처(Citation) |
VOL 15 NO. 06 PP. 1996 ~ 2011 (2021. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English 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. |
| 키워드(Keyword) |
Apriori Algorithm
Big-data analytics
Degree of visibility
Inductive reasoning
Keyword emergence map
Well-being
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| 파일첨부 |
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