FivaTech is a system that deduces data schemas and templates from input pages generated by a CGI program. It contains two modules: tree merging and schema detection. The tree merging module recognizes similar nodes using a 2-tree matching algorithm and computes a normalized score to merge trees. The schema detection module mines patterns from the merged trees to identify tuple types, element orders, and optional data. It then defines templates by segmenting the pattern tree at reference nodes.

1. FivaTech ： Schema & Template Discovery Reporter ： Che-Min Liao

2. Introduction FivaTech is a page-level data extraction system which deduces the data schema and templates for the input pages generated from a CGI program. Tree Merging Schema Detection

3. Problem Formulation

4. Problem Formulation

5. The FivaTech Approach The proposed approach FivaTech contains two modules ： Tree merging Schema detection

6. Peer Node Recognition As each tag/node is actually denoted a tree, we can use 2-tree matching algorithm for computing whether two nodes with the same tag are similar. We adopt Yang’s algorithm A more serious problem is score normalization. A typical way to compute a normalized score is the ratio between the numbers of parts in the mapping over the maximum size of the two trees.

7. Tree Merging Score Algorithm

8. Example

9. Peer Matrix Alignment

10. Pattern Mining

11. Optional Node Merging After the mining step, we are able to detect optional nodes based the ocurence vectors .

12. The Example of Pattern Tree

13. Identifying the Schema Recognize tuple type Recognize order of the set type and optional data.

14. Defining the Template Templates can be obtained by segmenting the pattern tree at reference nodes defined below ：

15. The Example of Schema

16. The Example of Template T( τ 1 ) = (T 1 , (T 2 , Φ ), 0) T( τ 2 ) = ( Φ , (T 3 , Φ ), 0) T( τ 3 ) = ( Φ , (T 4 , T 5 , T 21 ), (0,0)) T( τ 4 ) = ( Φ , (T 6 , T 7 , Φ ), (0,0)) … T( τ 13 ) = ( Φ , (T 20 , Φ ), 2)