Object Oriented Multi-Database SystemsAn Overview of Chapters 4 and 5

A multidatabase system is a distributed system that provides a global interface to heterogeneous pre-existing local DBMS’s• Users can access multiple remote databases with a single query• Automatically performs the data model and access language transformations between global

query and the local databases

Distributed databases • Maintain a global name space and some form of global schema• All local databases use the same data model and access language• A collection of cooperating, homogeneous local DBMS’s that provides a uniform global interface

Interoperable systems• No concept of a global schema/namespace• Provide formats and protocols for shipping data between local systems• Do not provide much global functionality• Loosely coupled

Multidatabases• Supports full/partial global schemas• Integrates heterogeneous, pre-existing local DBMS’s• Local databases can use different data model and access languages

What Are OOMDMS’s?What are some of their key differences?

• Tool requirements for successful integration of real-world schema’s:

• Assists users during integration

• Take into account users requirements and usability as the overriding considerations for the tool

• No changes to existing data and local schemas

• Users only have to deal with global semantic model

• Incremental schema integration capability

• Permit imprecise reasoning

• Automatic generation of mappings between global and local schemas

• Advantages of an Object-Oriented Data Model

• Class structures are specifically designed to support generalisation of lower level data classes

• Methods and polymorphism enable a rich set of functions to be applied to data objects

• Provides a very natural mechanism for translating to and from other data models

General Issues of Dealing with the Schema Integration Problem

• Identification of correspondences is non-trivial. Occurs due to:

• Syntactic differences

• E.g. Differences in names, domain, scale, data types

• Semantic differences

• E.g. Synonyms, Hyponyms, Antonyms

• Correspondence types

• Equivalence

• Containment

• Overlap

• Disjoint

• Others?

Nature of Problems in Schema Integration

Integration Process - Activities

• Application of reasoning techniques for the comparison of the schemas to generate correspondence assertions

• Validation of system-generated assertions by the user or specification of new assertions by the user

• Automatic generation of new assertions or deletion of existing assertions based on user validation of assertions

• Checking and ensuring the consistency of user validations and assertions

• Merging the objects according to the specified assertions and options

• Generation of mappings between the global schema and the component schemas

• Authors proposal of their Integration Tool, consists of:

• A set of invariant structures i.e. assumptions

• A set of validated assertions called facts

• A set of merging rules

• Advantages• Compared to other tools, the set of assumptions do not change

even when integration technique changes

• Tool is extensible due its modular architecture

• Imprecise reasoning module

• Consistency checking module

• User interface

• Mapping generator

Core Structures Central To Schema Integration

• People perceive real-world objects in different ways which leads to potentially different representations of the same object

• Semantics is relative i.e. different conceptualisations

• Example: Concept of Marriage in DB#1 represented by objects of the class COUPLES, with attributes HUSBAND and WIFE, whereas in DB#2 a class PERSONS with a SPOUSE attribute

Semantic Heterogeneity in Multidatabase Systems

Three main classification groups:• Heterogeneities between object classes

• Extensions, i.e. membership• Names i.e. Synonomy, polysemy• Class methods/attributes, and many more…

• Heterogeneities between class structures• Different generalisation hierarchy• Representing part-whole relationships

• Heterogeneities between object instances• Attributes allowing null/nonnull• Value discrepancies

Classification of Semantic Heterogeneity

Detecting Semantic Heterogeneity

• Aim is to identify semantically related objects by a comparison process in which their similarities and dissimilarities are found out

• (Early Schema Integration)Tools• SIS: A Schema Integration System• Honeywell Testbed• MUVIS

• A number of strategies exist for similarity detection• A Theory of Attribute Equivalence• Common Concepts Approach• Semantic Unification Approach• Maximum Spanning Tree Approach

Resolution of Semantic Heterogeneity

• After identifying semantically related objects, conflicts need to be resolved in order to gain integrated access to the multidatabase

• Several tools and systems exist (even more post 1996)

• Multibase• Honeywell Testbed• Carnot• More recently Coma++• …many more

Conclusion

• Semantic Heterogeneity is an obstacle for interoperability• Typically database schema’s do not provide enough

semantics• Most approaches adopt a semi-automatic approach to

detecting semantic similarity• Detection of semantic similarity is more difficult than

semantic resolution• Advantage of adopting an object-oriented data model is

its high expressiveness resulting in richer semantic models