[lecture] Data Analysis on Clouds by Jun Sup Lee (이준섭, KT/SW개발센터장)

Title	Data Analysis on Clouds
Place	KAIST Dogok Software Grad School Chin’s AMP Hall 103-ho
Time	October 30, 2014 5PM~6PM
Speaker	Jun Sup Lee (이준섭, KT/SW개발센터장)
Organizer	KAIST Software Graduate Program
Focus	KT's cloud solution,

I.       Message

1. impact of disruptive technology: after industrial revolution, many people lost jobs; likewise, after self-driving car, many taxi drivers may lose jobs as well
2. reason behind Google's Map business involvement: Google wanted to know where people live perhaps because the company local marketing piece of ad was missing in its product and revenue stream portfolio
3. data analysis process and examples on clouds
1. general rule of thumb: create summary-> mine data-> select machine learning method -> ?
2. ad display logic: cluster similar behaviors when ads are shown to users-> display similar ads matching the cluster
3. movie recommendation logic: vector -> choose similar users-> recommend similar movies
4. lesson from Melon: need to discern data's end results such as how users would response to product of data analysis before developing data analysis product e.g. users only care for top 10 rankings instead of relevant music to their tastes
4. new lingo: ETL (extract, transform and load)

II.      My Takeaway

1. need to work on jobs that will survive in 10 to 20 years
2. think end results before development