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Resolving Schematic Discrepancy in the Integration of Entity-Relationship Schemas

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Title: Resolving Schematic Discrepancy in the Integration of Entity-Relationship Schemas


1
Resolving Schematic Discrepancy in the
Integration of Entity-Relationship Schemas
  • Qi He Tok Wang Ling
  • Dept. of Computer Science
  • School of Computing
  • National Univ. of Singapore

2
Outline
  • Schema integration background
  • Schematic discrepancy
  • Representation of meta information in ER schemas
  • Resolution of schematic discrepancy in schema
    integration
  • Related work
  • Conclusion

3
Schema Integration
  • In DB integration, produce an integrated view
    which provides a unified access to heterogeneous
    data in source schemas.
  • In DB design, produce a global schema of a
    proposed DB by integrating user views in DB
    design.

4
Challenges in schema integration
  • Many types of conflicts among different source
    schemas need to be resolved in schema
    integration
  • Naming conflicts
  • Domain mismatch
  • Structural conflicts
  • Cardinality conflicts
  • Local constraints vs global constraints
  • (e.g. local vs global functional
    dependencies)
  • Schematic discrepancy

5
Schematic Discrepancy
  • Schematic discrepancy occurs when a metadata in
    one database corresponds to attribute values in
    the other.
  • An example (next page)
  • months and supplier numbers (i.e., S1, , Sn) are
    modeled differently as attribute values or schema
    labels (in general, metadata which will be
    introduced later) in databases DB1, DB2, and DB3.

6
Motivation Example
price is an attribute of the ternary relationship
type PMS
PM is a relationship type between product and
month
7
Contexts of schema constructs
  • Conceptual modeling is always done within a
    particular context which is explicitly
    represented as a set of meta attributes with
    values (called metadata).
  • Meta attributes with values specify the
    conditions satisfied by the instances of a schema
    construct (i.e., entity type, relationship type,
    or attribute).

8
Ontology
  • A representational vocabulary for a shared domain
    of discourse which includes the definitions of
    entity types, relationship types, and attributes.
  • We use an ontology to describe the meta
    information of the ER schemas of the supply
    example
  • Entity types product, supplier, month
  • Attributes of entity types p, pname, s, month
  • Relationship types
  • PMS (a ternary supply relationship type
    among product, month
  • and supplier)
  • PM (a binary relationship type between
    product and month) PM is a
    projection of PMS.
  • Attributes of relationship types price (an
    attribute of PMS)

9
Example of Context
  • In DB2, the entity type JAN_PROD is represented
    as
  • JAN_PROD PM month jan
  • where PM and month are resp. a relationship type
    and an entity type from the ontology.
  • It means that JAN_PROD is derived from the
    product-month binary relationship type (i.e. PM)
    when the month value is jan.
  • month is a meta-attribute and jan the metadata of
    JAN_PROD.

10
Inheritance of Context
  • Context could be specified at 4 levels of
  • Databases
  • Entity types
  • Relationship types
  • Attributes
  • The context of a higher level schema construct
    could be inherited by a lower level schema
    construct. The inheritance hierarchy of contexts
    is
  • relationship type ?
    attribute of relationship type
  • database ? entity type
  • attribute of entity type

11
Example of context inheritance
  • In DB2, the attribute S1_PRICE of the entity type
    JAN_PROD is represented as
  • S1_PRICE price suppliers1, inherit ALL
  • S1_PRICE inherits all, i.e. the context
    monthjan, from the entity type JAN_PROD.
  • The representation means that each value of
    S1_PRICE of the entity type JAN_PROD is a price
    of a product supplied by supplier s1 in the month
    of jan.

12
Resolution of schematic discrepancy in the
integration of ER schemas
13
  • Basic Idea Remove the contexts of schema
    constructs by transforming meta-attributes into
    entity types.
  • Only meta-attributes causing schematic
    discrepancy need to be transformed.
  • Schema transformation should keep the semantics
    (information and constraints) of source schemas.

14
  • Resolve schematic discrepancy for entity types,
    relationship types, attributes of entity types
    and attributes of relationship types in order
    (the order conforms to the hierarchical order of
    context inheritance).
  • The context at database level is handled in the
    entity types which inherit it.

15
An example
  • Transforming DB2 into DB1 in 2 steps
  • Step 1 Resolve discrepancies for the entity
    types JAN_PROD, , DEC_PROD
  • Step 1.1 Transform meta-attributes into entity
    types
  • Step 1.2 Merge equivalent entity types,
    relationship types and attributes
  • Step 2 resolve discrepancies for the attributes
    S1_PRICE, , SN_PRICE

16
PM is a relationship type between product and
month
  • Step 1.1 Transform the meta-attribute month of
    the entity type JAN_PROD (the other entity types
    are similar)
  • Construct an entity type MONTH to model the meta
    info
  • JAN_PROD becomes PROD after removing the context
  • Construct a relationship type PM to relate PROD
    and MONTH
  • Attributes S1_PRICE, , SN_PRICE are moved to PM,
    as they inherit the context (i.e., the month) of
    the entity type JAN_PROD.

17
Step 1.2 Merge the equivalent entity types,
relationship types and attributes which refer to
the same ontology names. Note the domains of the
MONTH attributes are united.
18
An example (cont.)
  • Transforming DB2 into DB1 in 2 steps
  • Step 1 Resolve discrepancies for the entity
    types JAN_PROD, , DEC_PROD
  • Step 2 Resolve discrepancies for the attributes
    S1_PRICE, , SN_PRICE
  • Step 2.1 Transform meta-attributes into entity
    types.
  • Step 2.2 Merge equivalent entity types,
    relationship types and attributes.
  • Step 2.3 Remove redundant relationship types.

19
price is an attribute of the relationship type PMS
  • Step 2.1 Transform the meta-attribute supplier
    of the attribute S1_PRICE (the other attributes
    are similar)
  • Construct an entity type SUPPLIER to model the
    meta information.
  • Construct a relationship type PMS to relate PROD,
    MONTH and SUPPLIER.
  • S1_PRICE becomes PRICE after removing the
    context, and is moved to PMS.

20
Step 2.2 Merge the equivalent entity types,
relationship types and attributes. The domains of
the S attributes are united.
21
Step 2.3 Remove the redundant relationship type
PM that is a projection of PMS.
22
Semantic preservation
  • Our solution to schematic discrepancy preserves
    the semantics of source schemas in schema
    transformation
  • Information preservation. The instance of a
    schema can be losslessly converted into the
    instance of another schema, and conversely.
  • Constraint preservation. Cardinality constraints
    of ER schemas can be preserved in schema
    transformation, but in different forms in the
    source and transformed schemas (an example is
    given in the next page).

23
Constraint Preservation (E.g.)
  • Functional dependency (FD) is preserved in the
    transformation from DB2 to DB1.
  • Suppose in each entity type JAN_PROD, , DEC_PROD
    of DB2, the FD holds
  • P?? S1_PRICE, , SN_PRICE
  • In DB1, the FD is preserved, but in a different
    form
  • P, S, MONTH?? PRICE
  • In 3, we gave inference rules to derive FDs in
    schema transformation.

3 Qi He and Tok Wang Ling Extending and
inferring functional dependency in schema
transformation. CIKM, 2004.
24
Related work
  • The definition of context as a set of
    meta-attributes with values is originally adopted
    in 2, 9.
  • They defined context at the attribute level only.
  • We consider contexts at the levels of database,
    entity types and attributes, as well as the
    inheritance of context.

2 C. H. Goh, S. Bressan, S. Madnick, and M.
Siegel Context interchange new features and
formalisms for the intelligent integration of
information. TOIS, 1999 9 E. Sciore, M. Siegel,
A. Rosenthal Using semantic values to facilitate
interoperability among heterogeneous information
systems, TODS, 1994
25
Related work
  • Existing work in schema integration focused on
    the resolution of structural conflicts 1, 7 and
    constraint conflicts 6, 8.
  • Our solution to schematic discrepancy complements
    those works.
  • The resolution of schematic discrepancy is
    followed by the resolution of other conflicts.

1 C. Batini, M. Lenzerini A methodology for
data schema integration in the Entity-Relationship
model. IEEE Trans. on Software Engineering,
10(6), 1984 6 Mong Li Lee, Tok Wang Ling
Resolving constraint conflicts in the integration
of entity-relationship schemas. ER, 1997 7 Mong
Li Lee, Tok Wang Ling A methodology for
structural conflicts resolution in the
integration of entity-relationship schemas.
Knowledge and Information Sys., 5, 2003 8 M.
P. Reddy, B.E.Prasad, Amar Gupta Formulating
global integrity constraints during derivation of
global schema. Data Knowledge Engineering, 16,
1995
26
Related work
  • Schematic discrepancy in relational model is
    solved in some multidatabase languages 4, 5.
  • They solved a special problem in schematic
    discrepancy they transform relation names or
    attribute names to attribute values, or converse.
  • They did not consider the constraint issue in
    schema transformation.
  • Our work solves a general problem, and preserves
    cardinality constraints of ER schemas in the
    schema transformation.

4 R. Krishnamurthy, W. Litwin, W. Kent
Language features for interoperability of
databases with schematic discrepancies. SIGMOD,
1991 5 L. V. S. Lakshmanan, F. Sadri, S. N.
Subramanian SchemaSQLan extension to SQL for
multidatabase interoperability. TODS, 2001
27
Conclusion
  • ER model supports cardinality constraints, which
    facilitates the derivation of constraints in
    schema transformation and integration.
  • Context is used to explicitly represent meta
    information of entity types, relationship types
    and attributes in ER schemas.
  • Schematic discrepancy is resolved by removing
    context.
  • The solution to schematic discrepancy preserves
    information and constraints.

28
Reference
  • 1 C. Batini, M. Lenzerini A methodology for
    data schema integration in the Entity-Relationship
    model. IEEE Trans. on Software Engineering,
    10(6), 1984
  • 2 C. H. Goh, S. Bressan, S. Madnick, and M.
    Siegel Context interchange new features and
    formalisms for the intelligent integration of
    information. ACM Transactions on Information
    Systems, 17(3), 1999, pp 270-293
  • 3 Qi He and Tok Wang Ling Extending and
    inferring functional dependency in schema
    transformation. CIKM, 2004.
  • 4 R. Krishnamurthy, W. Litwin, W. Kent
    Language features for interoperability of
    databases with schematic discrepancies. SIGMOD,
    1991, pp 40-49
  • 5 L. V. S. Lakshmanan, F. Sadri, S. N.
    Subramanian SchemaSQLan extension to SQL for
    multidatabase interoperability. TODS, 2001, pp
    476-519
  • 6 Mong Li Lee, Tok Wang Ling Resolving
    constraint conflicts in the integration of
    entity-relationship schemas. ER, 1997, pp 394-407
  • 7 Mong Li Lee, Tok Wang Ling A methodology for
    structural conflicts resolution in the
    integration of entity-relationship schemas.
    Knowledge and Information Sys., 5, 2003, pp
    225-247
  • 8 M. P. Reddy, B.E.Prasad, Amar Gupta
    Formulating global integrity constraints during
    derivation of global schema. Data Knowledge
    Engineering, 16, 1995, pp 241-268
  • 9 E. Sciore, M. Siegel, A. Rosenthal Using
    semantic values to facilitate interoperability
    among heterogeneous information systems, TODS,
    19(2), 1994, pp 254-290
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