세바시 15분 신철호 포스닥 대표이사, ogq 이사회 의장 - 미래예측. 쿼리를...
DESCRIPTION
E.H Carr(1892~1982)는 역사 속 걸출한 인물의 역할을 인정하며 1급 지도자는 사회 세력을 만드는 반면 2급은 이미 형성된 사회 세력에 편승한다 했습니다. 사회 세력을 만드는 1급 지도자는 어김 없이 ‘선각’ 속에서 미래를 그려왔습니다. 사회를 변화시키는 것은 결국 사람이라는 전제를 받아 들일 때, IT사회에서 1인의 선각에 의존하지 않고 미래 예측을 할 수 있는 대안은 무엇일까요? 다수 참여자(Social)의 생각의 나이테를 검색어(Query)로부터 추출하여 이를 미래 예측에 활용하고 혁신하는 사례와 우리가 도전해 볼 수 있는 미래예측의 대상을 분석해 봅니다.TRANSCRIPT
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2. , 3. OGQ 4. 5. , 6. 7. 8. Query 9. National Treasure 10. 11. 12. 13. 2 14. (Proof 15. & 16. Description) Query 17. GROWTH RING OF PEOPLE 18. , 19. 20. Vertical 21. GatheringPREDICTION 22. 1,638 1831,335 23. 2,334 24. 0.5~15.2~14.8 25. 26. 27. Needs, 28. Intention 29. & 30. EmotionQuery 31. based 32. Prediction 33. 34. 35. () () 36. Daniel Bell1960, The end of Ideology1973, The coming of Post_industrial SocietyPeter DruckerAlbin TofflerSamuel HuntingtonRaymond KurzweilAllen SinaiCEO, Decision Economics, IncAsia DymanicsLess Double dipGood in Stock, goldFinancial Crisis in Eurozone 37. Yoshida 38. Shoin (1830~1859)() (1833~1877) () (1839~1867) (1838~1922)() (1839~1867) , (1839~1867) , , () ( ) (2 ) , () ( ) . 39. 40. 41. 42. 43. 44. 45. 46. 47. , 48. 49. 50. 51. 52. 53. 54. 55. , 56. 57. 58. 59. 21, 60. 61. 62. 63. 64. Success , 65. 66. 67. 68. 69. 70. 71. 72. , 73. , 74. 75. 76. 77. 78. , 79. , 80. 81. 82. 83. 84. 85. 86. Fail , 87. DNA 88. 89. 90. 91. 92. 93. 21 94. 95. , 96. 1990 97. 98. 99. 100. 101. 102. 103. 104. 105. Foresight 106. 107. ? 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. Predict ForecastingForesee 123. 124. SAS KOREA KISTEP 125. 126. (probable) 127. 128. 129. 130. (extrapolation), 131. (plausible) 132. Glenn(1994) 133. 134. 135. (scenario), 136. (preferable) 137. 138. 139. 140. (planning)( 141. - 142. )x 143. (normative) 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. (exploratory) 159. Environmental 160. Scanning, 161. The 162. Delphi 163. Method, 164. The 165. Futures 166. Wheel, 167. Trend 168. Impact 169. Analysis, 170. Cross-Impact 171. Analysis, 172. Structural 173. Analysis, 174. The 175. Systems 176. Perspectives, 177. Decision 178. Modeling, 179. Statistical 180. Modeling, 181. Technology 182. Sequence 183. Analysis, 184. Relevance 185. Trees 186. and 187. Morphological 188. Cordeiro(2008)Analysis, 189. Scenarios, 190. Interactive 191. Scenarios, 192. Participatory 193. Methods, 194. Simulation 195. and 196. Games, 197. Genius 198. Forecasting, 199. Normative 200. Forecasting, 201. S&T 202. Road 203. Mapping, 204. Field 205. Anomaly 206. Relaxation 207. (FAR), 208. Text 209. Mining 210. for 211. Technology 212. Foresight, 213. Agent 214. Modeling, 215. State 216. of 217. the 218. Future 219. Index 220. (SOFI) 221. Method, 222. SOFI 223. System, 224. The 225. Multiple 226. Perspective 227. Concept, 228. A 229. Toolbox 230. for 231. Scenario 232. Planning, 233. Causal 234. Layered 235. Analysis. 236. 237. 238. 239. 240. 241. QUERYPrediction 242. 243. , 244. 245. , , , SK T-mapDaum, Naver, Google Navigation 246. 247. Daum, Naver, GoogleApp , 248. 249. 250. 251. 252. 253. 254. 255. ........ 256. Detecting inuenza epidemics using search engine query dataNature 457, 1012-1014 (19 February 2009)A comparison of model estimates for the Mid-Atlantic Region (black) against CDC-reported ILIpercentages (red), including points over which the model was t and validated. A correlation of 0.85was obtained over 128 points from this region to which the model was t, while a correlation of0.96 was obtained over 42 validation points. 95% prediction intervals are indicated. 257. Predicting the Present with GoogleTrends,Hal varian, Hyunyoung Choi, 2009 258. 259. 260. , 261. , 262. 263. 264. 265. 266. 267. 268. QUERYPrediction 269. 12 Edward.H.Carr(1892~1982)The 270. Best 271. Way 272. to 273. Predict 274. the 275. Future 276. is 277. to 278. Invent 279. it.Query 280. , 281. 282. 283. 284. Thank you.by I.Tiger Shin ([email protected])OGQ Corp.