1. Effective Variation Management for Pseudo Periodical Streams SIGMOD’07

2. outline INTRODUCTION RELATED WORK PGG(Pattern Growth Graph) EXPERIMENTAL RESULTS CONCLUSION

3. INTRODUCTION Data stream processing techniques are widely applied in many domains such as stock market analysis, road traffic control, weather forecasting and medical information management. Online

4. RELATED WORK Complexity of Value Type on-Availability Training Sets or Models High Requirements for Variation Management

5. RELATED WORK 隨時間變化

6. RELATED WORK Two important problem 1. It is hard to capture the periodical data with fixed size windows 2. comparing two periods with different data sizes and time lengths is even more difficult.

7. PGG(Pattern Growth Graph) Task Specification. Wave, Alarms, Evolution, Summary

8. PGG(Pattern Growth Graph)

9. PLRE: Given a data series T, produce the best linear representation such that the maximum error for any segment does not exceed the user specified threshold PLRK: Given a data series T, produce the best linear representation using predefined number of segments K.

10. PGG(Pattern Growth Graph)

11. time1234567891011121314 real7.007.187.387.617.818.028.869.7010.548.868.988.868.798.68 切 77.197.38 -0.010 0

12. PGG(Pattern Growth Graph) time1234567891011121314 real7.007.187.387.617.818.028.869.7010.548.868.988.868.798.68 切 7.007.207.417.61 (0.05)0.00(0.02)(0.03)0.00

13. PGG(Pattern Growth Graph) time1234567891011121314 real7.007.187.387.617.818.028.869.7010.548.868.988.868.798.68 切 7.007.207.417.617.81 (0.04)0.00(0.02) 0.00

14. PGG(Pattern Growth Graph) time1234567891011121314 real7.007.187.387.617.818.028.869.7010.548.868.988.868.798.68 切 7.007.207.417.617.828.02 (0.06)0.00(0.02)(0.03)(0.00)(0.01)0.00

15. PGG(Pattern Growth Graph) time1234567891011121314 real7.007.187.387.617.818.028.869.7010.548.868.988.868.798.68 切 7.007.237.457.687.918.138.36 (0.40)0.00(0.05)(0.07) (0.10)(0.11)0.00

16. PGG(Pattern Growth Graph) time1234567891011121314 real7.007.187.387.617.818.028.869.7010.548.868.988.868.798.68 切 7.007.317.627.938.248.558.86 (1.65)0.00(0.13)(0.24)(0.32)(0.43)(0.53)0.00

17. PGG(Pattern Growth Graph) 1.65/53.86=0.03063 !! increase the pattern P’s frequency

18. PGG(Pattern Growth Graph)

19. (3) If no segment matches, we say that P totally un-matches W. Generate new base pattern P’ add P’ to PGG; (2) If only j (j<k) segments match, we say that P partially matches W

20. PGG(Pattern Growth Graph) after fully matches test

21. PGG(Pattern Growth Graph)

23. Framework !!

24. EXPERIMENTAL RESULTS

26. SAX(Symbolic Aggregate approXimation) Discrete Wavelet Transform (DWT)

27. EXPERIMENTAL RESULTS

29. CONCLUSION 他的方法可以將有周期性的 wave 畫出 window Size 很棒