Semantic heterogeneity resolution in federated databases by metadata implantation and stepwise evolution

Transcription

1 Semantic heterogeneity resolution in federated databases by metadata implantation and stepwise evolution Authors: Goksel Aslan, Dennis McLeod Presented by Saili Kypa

2 Understanding Keywords: Semantic heterogeneity What is semantics? The study of relationships between words and how we construct meaning What is heterogeneity? Diversity. Semantic heterogeneity is when database schema or datasets for the same domain are developed by independent parties, resulting in differences in meaning and interpretation of data values.

3 Types of databses Centralised database What is a Federation? an organization or group within which smaller divisions have some degree of internal autonomy SECTION TITLE 2

4 Database Interoperability: What is interoperability? It is the ability of different entities of similar kind to communicate, exchange data, and use the information that has been exchanged. If these entities are databases it is database interoperability Schema evolution refers to the problem of evolving a database schema to adapt it to a change. Database integration implies the ability to access any source of data within or outside your enterprise and use this information.

5 Meta-data Folding Problem Given two independently maintained databases, one local and one remote, it is the process of importing and customizing remote database elements into the local database

6 Previous Approaches Global Schema Approach: Limitations: Too much and too broad global knowledge. large global structure to be maintained. Integration effort is very high. lack of flexibility in the relationships between component database elements.

7 Federated Database with semantic dictionary or ontology requires moderate amount of global knowledge global structures integration effort actual exchange Limitation: Difficulty of agreeing on a set of concepts and interconcept relationships in a federation environment that is dynamic

8 Multidatabases with a multidatabase language: The MDSL MRDSM (a prototype multidatabase system) contains capabilities To join data To broadcast over database schemas To flow data To aggregate data from various databases. It requires a comparatively small amount of global structures Limitation: High user effort

9 Our Approach: Schema implantation and semantic evolution,a stepwise integration scheme. Key Ideas: The canonical data model employed in the federation and its constructs are important in making information unit semantics explicit. An incremental integration and semantic heterogeneity resolution process is utilized. A partial integration scheme is used.

10 Schema implantation and semantic evolution Example:

11 Heterogeneous Semantic Data Model (HSDM) as the canonical data model It supports traditional object-based notions such as object identity, classes, attributes, and a set of semantic primitives An HSDM database is a collection of objects and relationships between objects. Sub-class super-class relationships : 1. Attribute defined 2. Predicate defined 3. User defined We assume that in HSDM each class has at most one super-class.

12 Attributes in HSDM represent significant aspects of classes. 1. Application attributes 2. Complementary attributes 3. Descriptor attributes Every non-primitive object class in HSDM has 1. A Class key, and 2. An instance key which are both user-defined.

13 Phases of schema implantation and semantic evolution The schema implantation and semantic evolution technique consists of three phases: (a) semantic clarification, (b) schema implantation, and (c) semantic evolution.

14 Semantic Clarification This phase transforms a component conceptual schema from its native data model into HSDM (schema transformation), and augments it with additional information (semantic enrichment)

15 Schema implantation phase This phase loosely integrates local and remote conceptual schema elements. It consists of two sub-phases: 1. Superimposing 2. Hypothesis specification.

16 The second sub-phase is termed hypothesis specification; here, a number of hypothetical relevance are specified between local and remote classes by means of harmonizers

17 A harmonizer is a persistent structure that associates a local abstract class with a remote abstract class for the purpose of investigating whether or not they are semantically related The associated local class and associated remote class of a harmonizer are termed semantic peers. Structure of Harmonizer

18 Semantic evolution phase Testing harmonizer Schema evolution More hypothesis specification

19 Final local conceptual schema after semantic evolution:

20 Implementation Implementation of a full-fledged mechanism which realizes our approach requires two components: (1) translators that transform database schemas from various data models to HSDM, and (2) a database tool that manages the interactions between HSDMmanaged databases Prototype database tool proposed is named the HSDM Mediator. HSDM Mediator was built in two consecutive parts. PDM data model HSDM Mediator

21 HSDM Mediator Implementation architecture

22 Possible solutions to the metadata folding problem

23 Conclusion In this paper, we have described an approach to the metadata folding problem. This approach is effective than many previous ones in terms of Global knowledge required Using global structure Integration Effort Staticity of relationships between concepts.

24 Questions? Thankyou