1. 2004/5/281 Approximate Counting of Frequent Query Patterns over XQuery Stream Liang Huai Yang, Mong Li Lee, Wynne HSU DASFAA 2004 Speaker:Ming Jing Tsai

2. 2 Introduction  Efficient approach to improve XML management system Cache frequently retrieved results Frequent query patterns  application Search engine XML query system

3. 3 Preliminaries  S = QPT 1,QPT 2, …,QPT N  Query pattern trees(QPT) Label:{ “ * ”, ” // ” } ∪ tagset  Rooted subtree(RST) root(RST) = root(QPT) RST V ’ QPT V, RST E ’ QPT E

4. 4 QPT book titleauthorprice book title author price fn ln book title section QPT 1 QPT 2 QPT 3 book titleauthorprice RST

5. 5 Approximate Counting  rst.count app ≧ (σ-ε)N  rst.count app ≧ rst.count true -Εn  XQuery stream divided into buckets of w =  bcurrent =

6. 6 D-GQPT 1 362 book title author 54 fn ln 7 8 section price title RST 3 book 1 382 titleauthorprice book titleauthorprice 1,2,-1,3,-1,8,-1

7. 7 D-GQPT 1 362 book title author 54 fn ln 7 8 section price title RST 3 book 1 382 titleauthorprice book titleauthorprice 1,2,-1,4,-1,9,-1

8. 8 ECTree 1 1 2 1 3 1 6 1 8 1 28 1 26 1 23 G join G rmlne = 1 38 1 36 G join G rmlne 1 4 3 1 5 3 1 68 G join G rmlne 1 7 6 G join G rmlne = 1 45 3 1 36 4 1 38 4 1 36 7 G join G rmlne 1 368

9. 9 Candidate Generation  Rightmost active leaf node expansion G rmlne ( )=  G join ( )= | = X j = i+1, …,N

10. 10 Prune  RST K+1 doesn ’ t exist in ECTree RST k+1.Δ = b current - β | RST K+1.tidlist| < β prune  RST K+1 exists in ECTree RST K+1.count app = RST K+1. count app +|RST K+1.tidlist| RST K+1.count app + RST k+1.Δ < b current prune  Join result with RST K+1  subtree induced by RST K+1

11. 11 AppXQSMiner

12. 12 AppXQSMiner

13. 13 ECTree 1 1 2 1 3 1 6 1 8 1 28 1 26 1 23 G join G rmlne = 1 38 1 36 G join G rmlne 1 4 3 1 5 3 1 68 G join G rmlne 1 7 6 G join G rmlne = 1 45 3 1 36 4 1 38 4 1 36 7 G join G rmlne 1 368

14. 14 Experiment  P4 2.4GHz, 1GB RAM, WINXP  DBLP DTD:98 nodes  Shakespears ’ Play DTD: 23 nodes

15. 15 Experiment error=0.1 σ

16. 16 Experiment error = 0.1 σ

17. 17 Experiment sup = 0.005

18. 18 Experiment sup = 0.005

19. 19 Experiment error = 0.05 σ

20. 20 Experiment error = 0.05 σ