1.  DATAABSTRACTION  INSTANCES& SCHEMAS  DATA MODELS

2. DATA ABSTRACTION

3. Physical Level Logical Level View Level

4. The lowest level of abstraction describes how a system actually stores data. The physical level describes complex low- level data structures in detail.

5. The next higher level of abstraction describes what data the database stores, and what relationships exist among those data. The logical level thus describes an entire database in terms of a small number of relatively simple structures. Although implementation of the simple structures at the logical level may involve complex physical level structures, the user of the logical level does not need to be aware of this complexity. This referred to as physical data independence. Database administrators, who must decide what information to keep in a database, use the logical level of abstraction.

6. The highest level of abstraction describes only part of the entire database. Even though the logical level uses simpler structures, complexity remains because of the variety of information stored in a large database. Many users of a database system do not need all this information; instead, they need to access only a part of the database. The view level of abstraction exists to simplify their interaction with the system. The system may provide many views for the same database.

7. INSTANCES & SCHEMAS

8. The environment of database is said to be instance. A database instance or an ‘instance’ is made up of the background processes needed by the database software. These processes usually include a process monitor, session monitor, lock monitor, etc. They will vary from database vendor to database vendor. A database instance (Server) is a set of memory structure and background processes that access a set of database files.

9. A database schema of a database system is its structure described in a formal language supported by the database management system (DBMS) and refers to the organization of data as a blueprint of how a database is constructed

10. Physical schema Logical Schema Schema

11. A physical data model (or database design) is a representation of a data design which takes into account the facilities and constraints of a given database management system. A physical schema is hidden beneath the logical schema, and can usually be changed easily without affecting application programs

12. A logical schema is a data model of a specific problem domain expressed in terms of a particular data management technology.

13. DATA MODELS

14. Underlying the structure of a databse is the data model: a collection of conceptual tools for describing data, data relationships, data semantics and consistency constraints.

15. Data Models Relational Entity- Relationship Object-Based Semistructured

16. The purpose of the relational model is to provide a declarative method for specifying data and queries: users directly state what information the database contains and what information they want from it, and let the database management system software take care of describing data structures for storing the data and retrieval procedures for answering queries.

17. An entity–relationship model (ER model) is a data model for describing the data or information aspects of a business domain or its process requirements, in an abstract way that lends itself to ultimately being implemented in a database such as a relational database. The main components of ER models are entities (things) and the relationships that can exist among them. Entity–relationship modeling was developed by Peter Chen

18. Object-oriented database management sytems allow object-oriented programmers to develop the product, store them as objects, and replicate or modify existing objects to make new objects within the OODBMS. Because the database is integrated with the programming language, the programmer can maintain consistency within one environment, in that both the OODBMS and the programming language will use the same model of representation. Relational DBMS projects, by way of contrast, maintain a clearer division between the database model and the application.

19. The semi-structured model is a database model where there is no separation between the data and the schema, and the amount of structure used depends on the purpose. This is in contrast to the data models mentioned earlier where every data item of a particular type must have the same set of attributes. Eg: XML

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