Datamodels & architecture
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This document discusses data models and the three schema architecture of database management systems (DBMS). It describes the three levels of schemas in a DBMS - physical schema, conceptual/logical schema, and external schemas. The three schema architecture supports program-data independence and multiple user views of data by providing different levels of abstraction and independence between the schemas.
Datamodels & architecture
- 1. DATA MODELS AND THREE SCHEMA ARCHITECTURE OF DBMS
- 2. Data Models • Data Model: A set of concepts to describe the structure of a database, and certain constraints that the database should obey. • Data Model Operations: Operations for specifying database retrievals and updates by referring to the concepts of the data model. Operations on the data model may include basic operations and user-defined operations. Slide 2-2
- 3. Categories of data models • Conceptual (high-level, semantic) data models: Provide concepts that are close to the way many users perceive data. (Also called entity-based or object-based data models.) • Physical (low-level, internal) data models: Provide concepts that describe details of how data is stored in the computer. • Implementation (representational) data models: Provide concepts that fall between the above two, balancing user views with some computer storage details. (Schema – a description of data in terms of a data model ) Slide 2-3
- 5. Three-Schema Architecture • Proposed to support DBMS characteristics of: • Program-data independence. • Support of multiple views of the data. – Schema – a description of data in terms of a data model Slide 2-5
- 6. Three-Schema Architecture • Defines DBMS schemas at three levels: • Internal schema at the internal level to describe physical storage structures and access paths. Typically uses a physical data model. • Conceptual schema at the conceptual level to describe the structure and constraints for the whole database for a community of users. Uses a conceptual or an implementation data model. • External schemas at the external level to describe the various user views. Usually uses the same data model as the conceptual level. Slide 2-6
- 7. Levels of Abstraction in a DBMS External Schema 1 External Schema 2 External Schema 3 Conceptual (logical) Schema Physical Schema Secondary Storage 7
- 8. Physical Schema • Storage space allocation for data and indexes • Record description for storage (sizes for data items) • Record placement • Data compression and data encryption techniques M.G. Erechtchoukova 8
- 9. Conceptual (Logical) Schema Example: Book (bid: char(2), title: char(50), author: char(20), price: decimal(5,2), av_q: integer) Customer (cid: char(2), l_n: char(25), f_n: char(25), a_c: char(30), phone: char(10)) Order (i_n: char(2), bid: char(2), cid: char(2), n_i: integer, d_of_sale: date) M.G. Erechtchoukova 9
- 10. External Schema • Multiple external schemas • In relational DBMS, External Schema contains views and relations from conceptual schema • Reduce complexity of DBMS for users • Support data security • Support data independence M.G. Erechtchoukova 10
- 11. Data Independence When a schema at a lower level is changed, only the mappings between this schema and higher-level schemas need to be changed in a DBMS that fully supports data independence. The higher-level schemas themselves are unchanged. Hence, the application programs need not be changed since they refer to the external schemas. Slide 2-11
- 12. Data Independence External Schema 1 External Schema 2 External Schema 3 Logical data independence Conceptual Schema Physical data independence Physical Schema Secondary Storage 12
- 13. Data Independence (cont…) • Logical data independence – the immunity of the external schemas to changes in the conceptual schema • Physical data independence – the immunity of the conceptual schema to changes in the physical schema M.G. Erechtchoukova 13