1. Object Oriented Multi-Database Systems An Overview of Chapters 4 and 5

2. 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?

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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