Translating Relational Schemas to XML Schemas

1. Translating Relational Schemas to XML Schemas Dongwon Lee, Murali Mani, Frank Chiu, Wesley. W. Chu UCLA

2. Our goals. • Map from relational to XML model automatically. • Maintain semantic constraints during the mapping. • Existing work such as XML Extender (from IBM), db2XML, XML-DBMS, SilkRoute, EXPERANTO rely on user-specified mapping, and do not maintain semantic constraints.

3. NeT: Nesting-based Translation • Nesting on a single attribute, X C, where C is column set of a table t “groups” the tuples of t with the same value for (C – X). • NeT is especially useful to: • Remove redundancies due to MVDs. • Group on attributes to get a more intuitive schema. • Semantic constraints are maintained.

4. nestC(t) t nestA(nestC(t))

5. NeT optimization • Perform minimum number of nesting. • Properties used for optimization: • Idempotency of nesting: we need not perform nesting more than once on any single attribute. • Nesting need not be done on an attribute that does not participate in any one candidate key. • Experimental results: NeT almost always decreases the data size tremendously – for one particular data set, size of the nested table is 6% of the size of the original table.

6. CoT: Constraint-based Translation: Step 1 • Consider IND s[]  t[], where   X,   Y,  is primary key, and  is non-nullable. • If  is unique, M(t) = (Y, s?), else M(t) = (Y, s*) • M(s) = (X - ) • Key for s is (Ks - ) M(professor) = (Pname, Age, student*) M(student) = (Sname, Course) <professor> <Pname>Muntz</Pname> <Age>60</Age> <student> <Sname>John</Sname> <Course>DB</Course> </student> <student> <Sname>John</Sname> <Course>N/W</Course> </student> </professor> student <professor> <Pname>Chu</Pname> <Age>55</Age> </professor> professor

7. CoT: Step 2 • Consider tables, s, t1, t2 with column set X, Y1, Y2, and INDs s[]  t1[1], and s[]  t2 [2], where 1, 2 are primary keys and  ,  are non-nullable • Translate one IND as in Step1, and translate the other to IDREF as: M(t1) = (Y1, s*), M(t2) = (Y2), M(s) = (X -  - ), A(t2) = {ID_t2::ID}, A(s) = {Ref_t2::IDREF} <professor> <Pname>Muntz</Pname> <Age>60</Age> <student Ref_course=“DB”> <Sname>John</Sname> </student> <student Ref_course=“N/W”> <Sname>John</Sname> </student> </professor> <course ID_course=“DB”/> <course ID_course=“N/W”/> <professor> <Pname>Chu</Pname> <Age>55</Age> </professor> course

8. CoT: Step 3 • Consider a relational schema with tables {t1, t2,…,tn} and INDs ti[i]  tj[j] • Construct an IND-graph • Identify top-nodes as: • Nodes that do not have any IND are top-nodes • In a strongly connected component formed from table-set, S, if there is no IND from a node in S to a node outside S, then one of the nodes in S must be a top-node. • Perform BFS and translate the IND as in Step 1 or Step 2.

9. M (course) = (Cid, Title, Room, course*) M(prof) = (student*) M(student) = (Sid, Name) M(emp) = (Eid, Name, dept*, proj*) M(dept) = (Dno) M(proj) = (Pname) A(emp) = {ID_emp::ID, Ref_proj::IDREF} A(prof) = {Ref_emp::IDREF} A(proj) = {ID_proj::ID} IND-Graph

10. Conclusions • Automatically map from relational to a good XML model. • Maintain semantic constraints. • Remove some of the redundancies that could have been present in the relational model.