Translating Relational Schemas to XML Schemas

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Translating Relational Schemas to XML Schemas

• Dongwon Lee, Murali Mani,
• Frank Chiu, Wesley. W. Chu
• UCLA

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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.

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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.

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

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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)
ltprofessorgt ltPnamegtMuntzlt/Pnamegt
ltAgegt60lt/Agegt ltstudentgt ltSnamegtJohnlt/Snamegt
ltCoursegtDBlt/Coursegt lt/studentgt ltstudentgt
ltSnamegtJohnlt/Snamegt ltCoursegtN/Wlt/Coursegt
lt/studentgt lt/professorgt
student
ltprofessorgt ltPnamegtChult/Pnamegt
ltAgegt55lt/Agegt lt/professorgt
professor
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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_t2ID, A(s)
  Ref_t2IDREF

ltprofessorgt ltPnamegtMuntzlt/Pnamegt
ltAgegt60lt/Agegt ltstudent Ref_courseDBgt
ltSnamegtJohnlt/Snamegt lt/studentgt ltstudent
Ref_courseN/Wgt ltSnamegtJohnlt/Snamegt
lt/studentgt lt/professorgt
ltcourse ID_courseDB/gt ltcourse
ID_courseN/W/gt ltprofessorgt
ltPnamegtChult/Pnamegt ltAgegt55lt/Agegt lt/professorgt
course
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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.

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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_empID,
Ref_projIDREF A(prof) Ref_empIDREF A(proj
) ID_projID
IND-Graph
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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.