1. 2004/12/31 報告人 : 邱紹禎 1 Mining Frequent Query Patterns from XML Queries L.H. Yang, M.L. Lee, W. Hsu, and S. Acharya. Proc. of 8th Int. Conf. on Database System for Advanced Applications(DASFAA’03)

2. 2 Motive As XML prevail, the efficient retrieval of XML Data become important many researches focus on 1. index XML documents 2. process regular path expression 3. discover frequent query pattern

3. 3 Query Pattern Tree Q 1 {resultPattern = {/book/title, /book/price}, predicates = {/book/author/data() = ”Buneman”}, documents = {book.xml}} Wildcards “*” indicate the ANY label in DTD Relative path “//”indicate zero or more labels

4. 4 Query Pattern Tree Def. Query Pattern tree A rooted tree QPT V is the vertex set E is the edge set Each vertex v has a label with its value in {“*”, “//”, tagSet} Def. Rooted Subtree A rooted subtree RST Root(RST)= Root(QPT) V’ V, E’ E

5. 5 Frequent Query Pattern Trees D : a database of query pattern trees {QPT 1,……,QPT N } Freq(RST) : the total occurrence of a rooted subtree RST in D Supp(RST) = freq(RST) / |D| The problem is to find all the frequent RSTs in D with some minimum support Supp(RST) = 2/3

6. 6 Tree Pattern Matching book/section/figure/title book // title book/section/figure/image book/section/*/image So node with label x ≦ * ≦ // Def. Query Pattern Tree Matching we say that RST is contained in a QPT if the following hold: 1. The root nodes in RST and QPT have the same label 2. If a node w RST is matched with node v QPT, then it satisfies (a)w.label ≦ v.label (b)each subtree of w is contained in some subtree of QPT

7. 7 Discovering Frequent Rooted Subtrees find all frequent 1-edge RSTs by scaning Database once RST-Gen generate the candidate set C k+1 by using the previously found frequent set F k and pruning those unqualified candidates. Contains determines if RSTk+1 is contained in the pattern tree t.

8. 8 Generation of Candidate RSTs use schema-guided enumeration method to generate candidate RST without repetition contruct a G-QPT by merging the query pattern tree in the database use Apriori property to prune the candidates RST

9. 9 Generation of Candidate RSTs

10. 10 Containment of RST in a Pattern Tree count the RSTs’ support in the database compare recursive from the root to the leaf node Algorithm Contains Case1 : w is a leaf node a) v.label is not ”//” 1) w.label = “//” or “*”, return the result of comparison w.label ≦ v.label 2) If w.label apprears in the set of labels of node v’s ancestors, return TRUE b) v.label is “//”, we must find if any of v’s child node n satisfies w.label ≦ n.label

11. 11 Containment of RST in a Pattern Tree Case2 : w is not a leaf node, and v is a leaf node w is impossible to be contained in v Case3 : Both w and v are not leaf nodes 1. if w.label ≦ v.label doesn’t hold, return false 2. compute whether all of the subtrees of w is contained in those of v 3. If v is “//” a) Check whether w is contained in one of v’s children b) Check whether the subtree of w is contained in v

12. 12 Containment of RST in a Pattern Tree-Example

13. 13 Optimizations for XQPMiner Encoding Query Pattern Trees Replaced by “1,2,-1,3,-1,8,-1” Indexing Frequent RSTs Using Transaction IDs Divide the enumeration of RST k into two sets 1. G leaf : generated by expanding the right most leaf node 2. G internal : generated by expanding the nodes along the right most branch except the leaf node

14. 14 Optimizations for XQPMiner- Example RST k+1.TIDList = RST k.TIDList ∩ RST 1 k.TIDList the RSTs in G internal need not be matched in D

15. 15 Performance Study P4 2.4GHz with 1GB RAM, running Windows XP Each dataset consist of 200000 QPTs Zipfian distribution Datasets DBLP Shakespears Play G-QPT Num. of nodes9867 Max depth86 Num. of //130 Max fanout129 QPT in DB Ave # of nodes7.47.5 Max depth86 Max fanout129

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18. 18 Algorithm Contains

19. 19 Algorithm Contains