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.

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.
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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

4. Data Models
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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.
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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
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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)
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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
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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.
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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
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