1. Efficient mining and prediction of user behavior patterns in mobile web systems Vincent S. Tseng, Kawuu W. Lin Information and Software Technology 48 (2006) 357–369 69821002 朱玉棠 69821016 黃弓凌 69821028 張治軍

2. Outline Introduction & system architecture Mining of sequential mobile access patterns-SMAP Prediction strategies Experimental evaluation Conclusions & associated thinking

3. Introduction What benefits for effectively modeling the behavior patterns of users? To help the user get desired information in a short time behavior patterns: a sequence of requests of a user form a location- service stream

4. Introduction

5. System architecture

6. SMAP-MINE:Construction of SMAP- Tree User IDAccess pattern 123456123456 SMAP-Tree SR-Tree(service request tree)

7. SMAP-Mine algorithm Threshold: δ (30%→6x0.3=2)

8. SMAP-Mine algorithm

9. CMAP-Mine 3 c:2 B:A: 8:2

10. SMAR prediction Sequential mobile access rules SMAR-Location SMAR-Service SMAR-L&S Strength = sup * conf ( RHS = LHS * conf )

11. SMAR prediction Because the number of generated rules might be huge, we create a corresponding hashing tree to accelerate the access. LHS 決定 hash value RHS is calculated by multiplying support and confidence root … LHS1 LHS2 RHS

12. SMAR prediction SMAR-N-gram Ex1: a historical behavior is set n = 2, the last two pair location-services pair plus current location now at location d, as LHS Ex2:a historical behavior is set n = 2, the last two pair location-services pair as LHS 20 5 (e,20) (d,5)

13. Experimental evaluation Probability of backward movement, P b = 0.1 Probability of next node movement: P n = 0.2 Probability of staying in the same node: P s = 0.3

14. Experimental evaluation

18. Conclusions & associated thinking The proposed data mining method, namely SMAP-Mine One physical scan on the database is needed The prediction function : SMAR-N-gram, which is based on the N-gram model

19. Conclusions & associated thinking Mining and predicting behaviors of driver for: Drunk driving Car racing etc…