tut_pfi_2012

1. 2012/06/12 Preferred Infrastructure

2. l l Preferred Infrastructure l l l l l Jubatusl

3. l l Preferred Infrastructure l l l l l Jubatus

4. Preferred Infrastructure (PFIl l 20063l l 26+ 7l 2-40-1 l / l l NHKNIIBPl l l Jubatus

5. PFI Bazil l l SPAMTwitterEC Sedue l l Web Jubatus l l SNSPOS l

6. Sedue l BP l asahi.com l l NHK l XAPPYEC/Web l l Webcat Plus l l

7. l l PFI l javascript, ash, rails, haskell, l

8. PFIl l l IT l l l l l

11. BigData ! l l 45%2020 4035ZB [Digital Universe 2010] l 3V Volume, Variety Velocity l l PC EC 11

12. BigDatal BigDatal l Jim Gray(1) (2) (3) (4) l l l l

13. l l Google, Amazon, Facebook l l l l l l 13

14. 3STEPSTEP 1. STEP 2. STEP 3. lStep1 Step2 30 30 14

15. l l l l l l l 15

16. 1/2l NYproactive maintenance l 1500 l l 300realtime/semi-realtime/static l l l MTBF (Mean time between failures) l Machine Learning for the New York City Power Grid, J. IEEE Trans. PAMI, to appear, Con Edison 16

17. 2/2l Reactive Maintenance l l l l l l l DB 17

20. l l l 20

21. (0, 1, 0, 2.5, -1, ) /SVM, LogReg, (1, 0.5, 0.1, -2, 3, ) PA, CW, ALOW, Nave Bayes (0, 1, 0, 1.5, 2, ) CNB, DT, RF, ANN, K-means, Spectral Clustering, MMC, LSI, LDA, GM, HMM, MRF, CRF, 21

22. l l l l l l 22

23. l l 2 1 IT 1 1 1 1 0.7 150 23 0

24. xy / y y DB 24

25. l l 25

26. 1ECl l l l l l l l l 26

27. 2l l l l l l l l 27

28. 3l l l l l l l l l 28

29. 4l SNS l l l // l l l l l l 29

30. +l Google, MS, Yahoo! l l l l

31. l l MRF CRF HMM , , l MAPl

32. l l l l l l l Residual Splash Belief Propagation [J. E. Gonzalez AISTATS 2009] l GraphLab [Y. Low et. al. UAI 2010]

33. [S. Singh LCCC 2010] 50 250 5x l NY Times 20100l 250

34. l l F() l SVM : F() = iLhinge() + C2l MapReduce l l l l l IterativeParameterMixture

35. Parameter Mixture1. Kshard2. shard3. shard l = (ii)/Kl l l

36. 2 Distributed Gradientl l l l l l shard l

37. 3Asynchronous Updatel l l lock

38. 4 Iterative Parameter Mixture[Mann et al 09][Mcdonald et. Al. 10]l Parameter Mixture1. shard2. shard3. 4. shard1 epoch Shard

39. 1 [K. Hall LCCC 2010] 37000 200 240workerMapReduce Iterative Parameter Mixture70

40. 2 [K. Hall LCCC 2010] Single-node 16 900 600workerMapReduce Iterative Parameter Mixture

42. Jubatus42

43. Jubatusl l 1) l 2) l 3)l (MapReduce/Hadoop l l l l / (CEP l l 43

44. Jubatus l NTT SIC*Preferred Infrastructure l 201110OSS http://jubat.us/ 44* NTT SIC: NTT

45. 1: / l l / l twitter6000QPSl l l 45

46. 1: / l l / l twitter6000QPSl l l 46

47. 2: l l l l l 47

48. l l l l l 48

49. Jubatusl l l l l l 49

50. l l l l Jubatus l l Perceptron (1958) l Passive Aggressive (PA) (2003) l Confidence Weighted Learning (CW) (2008) l AROW (2009) l Normal HERD (NHERD) (2010) 50

51. l l 51

52. l Jubatusl l l l l mix 52

53. Jubatus l l l 53

54. Jubatusl l l MapReduceShufflel l l l

55. l Jubatus l l c.f. MapReduceMapReducel UPDATE l l ANALYZE l l MIX l 55

56. UPDATE l l 56

57. ANALYZE l l 57

58. MIX l l l 58

59. l l (sum)(count)l UPDATE l sumi += x l counti += 1l ANALYZE l return (sum / count)l MIX l sum = sum1 + sum2 l count = count1 + count2 59

60. l l l 100% l l l MIX 60

61. Jubatusl , l l :Perceptron, PA, CW, AROWl l :PAl l Inverted File Index, LSHl l 61

62. Jubatusl Jubatusl Model lm1 lm2 lm3 l (lm) l (sm sm sm sml Update l lm x lm lml Analyze l x lm sm yl Mix l lm lm sm sm sm

63. + l Model l lm : wlocalRm l sm : wshareRml Update l wlocal, : x Rm, :y{+1, -1} l wlocal := wlocal + yxl Analyze l (wlocal + wshare)Tx 0 +1-1l Mix l wlocalwsharel Regretl

64. l Model l lm : CHT xlocalRm l sm : bit signature (LSH, minhash bshareRkl Update l xxsignaturel Analyze l bsharel Mix l xlocalbit signature

65. Jubatusl l l l l l l l l

66. Jubatusl Jubatusl C++l Pythonl Rubyl Javal l IDL l Haskelmsgpack idl 66

67. Jubatusl l l l l l l l l l l Jubatus 67

68. 68

69. 1/2l l l l l l l l l l l

70. 2/2l l l l OpenXC Project l ArduinoAndroidOSS l JSONl

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