Undergraduate
Database Management
Systems
Lecture 3
Hamza Shaukat
Hamza.Shaukat@itu.edu.pk
Information Technology University (ITU)
Faculty of Engineering
Data Models
Data Model
• A set of concepts to describe the structure of a
database, the operations for manipulating these
structures, and certain constraints that the
database should obey.
Data Model Structure and Constraints
▪ Constructs are used to define the database structure
▪ Constructs typically include elements (and their data types) as
well as groups of elements (e.g. entity, record, table), and
relationships among such groups
▪ Constraints specify some restrictions on valid data; these
constraints must be enforced at all times
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Data Models
Data Model Operations
▪ These operations are used for specifying database
retrievals and updates by referring to the constructs of
the data model.
▪ Operations on the data model may include basic model
operations (e.g. generic insert, delete, update) and user-defined
operations (e.g. compute_student_gpa, update_inventory)
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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. These are usually specified in an ad-hoc manner
through DBMS design and administration manuals
Implementation (representational) data models
▪ Provide concepts that fall between the above two, used by many
commercial DBMS implementations (e.g. relational data models used in
many commercial systems).
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Data Models
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Example of Conceptual Diagram
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Schemas versus Instances
Database Schema
▪ The description of a database
▪ Includes descriptions of the database structure, data types,
and the constraints on the database.
Schema Diagram
▪ An illustrative display of (most aspects of) a database schema.
Schema Construct
▪ A component of the schema or an object within the schema, e.g.,
STUDENT, COURSE.
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Schemas versus Instances
Database State
▪ The actual data stored in a database at a particular moment in
time. This includes the collection of all the data in the database.
▪ Also called database instance (or occurrence or snapshot).
– The term instance is also applied to individual
database components, e.g. record instance, table
instance, entity instance
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Database Schema vs. Database State
Database State
▪ Refers to the content of a database at a moment in time.
Initial Database State
▪ Refers to the database state when it is initially
loaded into the system.
Valid State
▪ A state that satisfies the structure and constraints
of the database.
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Database Schema vs. Database State
Distinction
▪ The database schema changes very infrequently.
▪ The database state changes every time the database is updated.
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Example of a Database Schema
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Example of a database state
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Database state
Which features of the database approach use the database state?
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Three-Schema Architecture
Proposed to support DBMS characteristics of
▪ Program-data independence.
▪ Support of multiple views of the data.
Not explicitly used in commercial DBMS products,
but has been useful in explaining database system
organization
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Three-Schema Architecture
Defines DBMS schemas at three levels
Internal schema at the internal level to describe physical storage structures
and access paths (e.g indexes).
– 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 schema.
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The three-schema architecture
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Three-Schema Architecture
Mappings among schema levels are needed to
transform requests and data.
▪ Programs refer to an external schema, and are
mapped by the DBMS to the internal schema for
execution.
▪ Data extracted from the internal DBMS level is
reformatted to match the user’s external view
(e.g. formatting the results of an SQL query for
display in a Web page)
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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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Data Independence
Logical Data Independence
The capacity to change the conceptual schema without
having to change the external schemas and their associated
application programs.
Physical Data Independence
▪ The capacity to change the internal schema without having
to change the conceptual schema.
▪ For example, the internal schema may be changed when
certain file structures are reorganized or new indexes are
created to improve database performance
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DBMS Languages
Data definition language (DDL)
• Defines both schemas
Storage definition language (SDL)
• Specifies the internal schema
View definition language (VDL)
• Specifies user views/mappings to conceptual
schema
Data manipulation language (DML)
• Allows retrieval, insertion, deletion, modification
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DBMS Languages (cont'd.)
High Level or Non-procedural Language:
▪ For example, the SQL relational language
▪ Are “set”-oriented and specify what data to retrieve
rather than how to retrieve it.
▪ Also called declarative languages.
Low Level or Procedural Language:
▪ Retrieve data one record at a time;
▪ Constructs such as looping are needed to retrieve
multiple records, along with positioning pointers
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