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Semantic Trajectory Mining for Location Prediction

This paper explores the use of semantic trajectory mining for predicting users' future locations based on their past movements. The authors propose a framework that combines data preprocessing, semantic mining, and geographic mining techniques to make accurate predictions. The effectiveness of the framework is evaluated through experiments using a real-world dataset. The results show promising results in terms of location prediction accuracy.

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Semantic Trajectory Mining for Location Prediction

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  1. Semantic Trajectory Mining for Location Prediction Josh Jia-Ching Ying1, Wang-Chien Lee2, Tz-Chiao Weng1and Vincent S. Tseng1 1 Department of Computer Science & Information Engineering, National Cheng Kung University, Taiwan, ROC 2 Department of Computer Science & Engineering Pennsylvania State University, PA 16802, USA

  2. Outline • Introduction • Location Prediction • Data Preprocessing • Semantic Mining • Geographic Mining • Matching Strategy & Scoring Function • Experiments • Conclusions

  3. Application Background ? ? ? ? • Location based services • navigational services • traffic management • location-based advertisement • Predict next location • Effective marketing • Efficient system operation 3

  4. Research Motivation • Frequent Pattern based Prediction Model • Frequent movement behavior of users • Geographic features of user trajectories • geographic properties • Distance • Shape • Velocity • … 4

  5. An example Trajectory Trajectory Trajectory Trajectory 1 1 3 3 Geographic Point Geographic Point Trajectory Trajectory 2 2 5

  6. An example 6

  7. Semantic trajectory Pattern • Frequent Pattern based Prediction Model • Frequent behaviors of users • Frequent movement behavior • Geographic features of user trajectories • Semantic trajectory • Frequent semantic behavior 7

  8. Outline • Introduction • SemanPredict framework • Data Preprocessing • Semantic Mining • Geographic Mining • Matching Strategy & Scoring Function • Experiments • Conclusions 8

  9. Framework 9

  10. Data Preprocessing To transforms each user’s GPS trajectories into stay location sequences. The stay location is a location where users stops for a while. Most activities of a mobile user are usually performed at where the user stays. 10

  11. Data Preprocessing Trajectory1 Trajectory3 Stay Point Trajectory2 Recommending Friends and Locations Based on Individual Location History Y. Zheng, L. Zheng, Z. Ma, X. Xie, W. Y. Ma VLDB Journal 2010 Stay Location1 Stay Location2 Stay Location3 11 Intelligent Database Laboratory, CSIE, NCKU - 11 -

  12. Data Preprocessing Stay Location2 Stay Location3 Stay Location1 Stay Location4 Stay Location5 Trajectory1 Trajectory2 Stay Location6 Trajectory3 12 Intelligent Database Laboratory, CSIE, NCKU - 12 -

  13. Framework 13

  14. Mining User Similarity from Semantic Trajectories. In Proceedings of LBSN' 10. 14

  15. Semantic Trajectory Pattern • Minimum support = 60% • Support(<School, Park>) = 2/3 > 60% • <School, Park> is a semantic trajectory pattern 15

  16. Semantic Trajectory Pattern Tree root <E,3> <B,6> <D,5> <A,4> <C,3> <D,3> <E,3> <B,3> <C,3> <C,3> 16

  17. Framework 17

  18. 18

  19. Framework 19

  20. Matching Strategy & Scoring Function Scoring Function Matching Strategy outdated moves may potentially deteriorate the precision of predictions. more recent moves potentially have more important impacts on predictions . the matching path with a higher support and a higher length may provide a greater confidence for predictions. 20

  21. Geographic Score and Candidate Paths User current movement: <Stay Location User current movement: <Stay Location3 , Stay Location , Stay Location0 , Stay Location , Stay Location1 > > ( , ) ( , ) ( ( Stay Location Stay Location , 0.7) ( ( Stay Location Stay Location , 1.0) , 1.0) 0 0 1 1 ( ( Stay Location Stay Location , 0.667) , 0.667) ( ( Stay Location Stay Location , 0.667) , 0.667) ( ( Stay Location Stay Location , 0.667) , 0.667) 1 1 3 3 3 3 ( ( Stay Location Stay Location , 0.667) , 0.667) 3 3 21

  22. Geographic Score and Candidate Paths User current movement: < User current movement: < Stay Location0 , Stay Location , Stay Location1 > > ( , ) ( , ) ( ( Stay Location Stay Location , 0. 7) ( ( Stay Location Stay Location , 1.0) , 1.0) 0 0 1 1 ( ( Stay Location Stay Location , 0.667) , 0.667) ( ( Stay Location Stay Location , 0.667) , 0.667) ( ( Stay Location Stay Location , 0.667) , 0.667) 1 1 3 3 3 3 ( ( Stay Location Stay Location , 0.667) , 0.667) 3 3 22

  23. Geographic Score and Candidate Paths User current movement: < User current movement: < Stay Location1 > > ( , ) ( , ) ( ( Stay Location Stay Location , 0.7) ( ( Stay Location Stay Location , 1.0) , 1.0) 0 0 1 1 ( ( Stay Location Stay Location , 0.667) , 0.667) ( ( Stay Location Stay Location , 0.667) , 0.667) ( ( Stay Location Stay Location , 0.667) , 0.667) 1 1 3 3 3 3 ( ( Stay Location Stay Location , 0.667) , 0.667) 3 3 23

  24. Candidate Paths Transformation α=0.8 24

  25. Semantic Score 25

  26. Outline • Introduction • Location Prediction • Semantic Mining • Geographic Mining • Matching Strategy & Scoring Function • Experiments • Conclusions 26

  27. Experiments • MIT reality mining dataset • The Reality Mining project was conducted from 2004-2005 at the MIT Media Laboratory • 106 mobile users • 14391 Trajectories • Cell span • Cell name 27

  28. Experiments • Sensitivity Tests 28

  29. Experiments • Impact of the semantic clustering 29

  30. Experiments Geographic Only: GO Full-Matching: FM Comparison of Prediction Strategies 30

  31. Experiments Efficiency Evaluation 31

  32. Outline • Introduction • Location Prediction • Semantic Mining • Geographic Mining • Matching Strategy & Scoring Function • Experiments • Conclusions 32

  33. Conclusions • A novel framework to predict the next location of a mobile user in support of various location-based services • both semantic and geographic information • A novel cluster-based prediction technique to predict the next location of a mobile user 33

  34. Thank you for your attention Quetion? 34

  35. MSTP-Similarity • Similarity of two users: user V user U P1 … Pm P1’ … Pn’ There are m×n MSTP-Similarity 35

  36. Semantic Trajectory Pattern • Semantic trajectory • Geographic semantic information database • a customized spatial database which stores the semantic information of landmarks that we collect via Google Map • <Stay Location3, Stay Location1, Location2> • <Restaurant, Park , School> • Frequent Pattern • Prefix-Span 36

  37. MSTP-Similarity • the ratio of common part 37

  38. MSTP-Similarity • Similarity of two patterns

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