Lecture 8 - Database Schema.pptx

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ICT 321

DATABASE SCHEMA

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Agenda

• Data model• ANSI/Spark Data Model• Database schema and instances

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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 (continued)• 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_average_mark, 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

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Categories of Data Models• 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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ANSI/Spark Data Model• Developed by American National Standard

Institute/ Standards Planning and Requirements Committee)

• Also known as ANSI-SPARC Three-Level Architecture

• This model provides some level of data abstraction and data independence

• Data abstraction generally refers to the suppression of details of data organization and storage, and the highlighting of the essential features for an improved understanding of data

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ANSI/Spark Data Model• The different levels(schema) of data abstraction

are:1. Physical level or internal level2. Logical level or conceptual level3. View level or external level

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ANSI/Spark Data Model

ANSI/Spark three level architecture diagram

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ANSI/Spark Data Model• Data independence means the internal

structure of database should be unaffected by changes to physical aspects of storage I.e. the DBA can change the database storage structures without affecting the users view.

• The data independence can be both logical and physical

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ANSI/Spark Data Model• 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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ANSI/Spark Data Model• 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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ANSI/Spark Data Model• 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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ANSI/Spark Data Model• The objective of this architecture is to

separate each user’s view of the database from the way the database is physically represented. There are several reasons why this separation is desirable:1. Each user should be able to access the same

data, but have a different customized view of the data.

2. Users should not have to deal directly with physical database storage details

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ANSI/Spark Data Model3. The DBA should be able to change the database

storage structures without affecting the users’ views.

4. The internal structure of the database should be unaffected by changes to the physical aspects of storage, such as the changeover to a new storage device.

5. The DBA should be able to change the conceptual structure of the database without affecting all users.

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View level or external level• The users’ view of the database. • This level describes that part of the database

that is relevant to each user.• Each external schema describes the part of

the database that a particular user group is interested in and hides the rest of the database from that user group.

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View level or external level- example

Auto insurance database: external schema.

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Logical level or conceptual level• The community view of the database.• This level describes what data is stored in the

database and the relationships among the data.

• This level contains the logical structure of the entire database as seen by the DBA.

• The conceptual model represents a global view of the entire database as viewed by the entire organization.

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Logical level or conceptual level• That is, the conceptual model integrates all

external views (entities, relationships, constraints, and processes) into a single global view of the data in the enterprise.

• The conceptual level represents:– all entities, their attributes, and their

relationships;– the constraints on the data;– semantic information about the data;– security and integrity information.

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Logical level or conceptual level

Auto insurance database: conceptual schema

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Physical level or internal level• The physical representation of the database

on the computer. This level describes how the data is stored in the database.

• The internal level is concerned with such things as:– storage space allocation for data and indexes;– record descriptions for storage (with stored sizes

for data items);– record placement;– data compression and data encryption techniques.

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Physical level or internal level• Below this level there is where data are

managed by the operating system under the direction of the DBMS

• The internal schema uses a physical data model and describes the complete details of data storage and access paths for the database.

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Physical level or internal level

Auto insurance database: internal schema.

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Database Schemas• The description of a database.• The overall design of the database is called the

database schema• It is a collection of named objects which

provide a logical classification of objects in the database.

• It Includes descriptions of the database structure, data types, and the constraints on the database.

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Database Schemas

• A schema is also an object in the database. It is explicitly created using the CREATE SCHEMA statement with the current user recorded as the schema owner.

• 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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Database SchemasSchema classification

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Database Schemas - example

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Database Schema - example

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Database 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 state - example

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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.