© michael lang, national university of ireland, galway 1 introduction to database systems

© Michael Lang, National University of Ireland, Galway1

Introduction to

Database Systems


Today’s session• Some history

• Origins of DB

• Organisational structure

• Organisational information systems

• Data storage

• DBMS

• Data models


What is “Data”?• ANSI definition:

Data A representation of facts, concepts, or instructions in a

formalized manner suitable for communication, interpretation, or processing by humans or by automatic means.

Any representation such as characters or analog quantities to which meaning is or might be assigned. Generally, we perform operations on data or data items to supply some information about an entity.

• Volatile vs. persistent data Our concern is primarily with persistent data


Early Data Management - Ancient History

• Data are not stored on disk• Programmer defines both logical data structure and physical

structure (storage structure, access methods, I/O modes, etc)• One data set per program. High data redundancy.

PROGRAM 2

DataManagement

PROGRAM 3

DataManagement

PROGRAM 1

DataManagement

DATA SET 1

DATA SET 2

DATA SET 3


Problems• There is no persistence.

All data is transient and disappears when the program terminates.

• Random access memory (RAM) is expensive and limited All data may not fit available memory

• Programmer productivity low The programmer has to do a lot of tedious work.


File Processing - Recent (and Current)

History• Data are stored in files with interface between programs and files.• Various access methods exist (e.g., sequential, indexed, random)• One file corresponds to one or several programs.

PROGRAM 1

DataManagement FILE 1

FILE 2 Red

unda

nt D

ata

PROGRAM 2

DataManagement

PROGRAM 3

DataManagement

FileSystemServices


File System Functions

• Mapping between logical files and physical files Logical files: a file viewed by users and programs.

• Data may be viewed as a collection of bytes or as a collection of records (collection of bytes with a particular structure)

• Programs manipulate logical files Physical files: a file as it actually exists on a storage

device.

• Data usually viewed as a collection of bytes located at a physical address on the device

• Operating systems manipulate physical files.

• A set of services and an interface (usually called application independent interface – API)


Problems With File Systems

• Data are highly redundant sharing limited and at the file level

• Data is unstructured “flat” files

• High maintenance costs data dependence; accessing data is difficult ensuring data consistency and controlling access

to data

• Sharing granularity is very coarse

• Difficulties in developing new applications


PROGRAM 1

PROGRAM 1

PROGRAM 2

IntegratedDatabase

DBMS

Query Processor

Transaction Mgr

…

Database Approach


Origins of Databases• c1455: Gutenberg invents printing. Explosive interest

in publication of books (analogous with explosive growth of Web in early 1990s) leads to public libraries

• Libraries were first to introduce standards for information storage and retrieval

• These paper-based systems were extended and enhanced, and filing, indexing and classification schemes were developed

• Second World War: accelerated R&D in computing technologies spawned capability to computerise maintenance of records


Computerised Data Storage

• Advantages of computerised data storage over paper-based systems include: ability to store data compactly (e.g Britannica CD) enhanced data retrieval ability to access data remotely, e.g. from a mobile

workstation, off-site location, or distant branch ability to share data amongst multiple users with

concurrent access facility to automate regular, speedy back-ups enhanced data editing

• Most significant disadvantage is vulnerability e.g system crash, corrupted data, viruses, hackers, etc.


Information: A Corporate Asset

• Information is a vital corporate asset. Without accurate, current, relevant information, mistakes and misjudgements may be made

• Data management is an essential capability in the modern business environment / information society

• A knowledge organisation is one in which the primary asset is information; its competitive advantage is derived from effective use of documented knowledge. Examples: accounting firms, marketing companies, software houses

• Organisational memory extends and amplifies information / knowledge by capturing, organising, disseminating, and reusing it


Organisational MemoryOrganisational Memory

Field(part of a record reserved for a particular data item)

File / Table(collection of related records)

Database(logical grouping of related files)

Record(collection of related fields, bound together as single units)

Byte(group of eight binary bits - can represent a single character)


Organisational Memory

Organisational memory

Data

Improved products and

services

Informed decisions


Attributes of Organisational Memory

• Current

• Timely real-time systems are commonplace in modern

business environment

• Relevant data is only useful if relevant to task in hand

• Shareable

• Complete

• Accurate and consistent


Attributes of Organisational Memory

• Transportable authorised personnel should have access to data

anywhere, anytime

• Secure against unauthorised access


Organisational Information Systems

• Generally, there is an alignment between business units and core operational systems

• Typical core systems are: Sales & Marketing Department: Customer

management system, Order processing system

Operations Unit: Purchasing system, Inventory control system

Finance Department: Accounts payable and receivable systems, Credit Management system

• There are interdependencies between these systems; hence the need for an integrated data management approach


Data Storage• Information systems create, read, update and

delete data

• Data can be stored in conventional files or databases Files are collections of similar records

Databases are collections of interrelated files. • Records can be linked through specified relationships to

records in other files


Conventional Files• In the file environment, data

storage is built around the applications that will use the files

• Essentially, the file “belongs” to a specific application. This is termed program-data dependence

• As applications are developed, customised files are created which may be unusable by other applications

InformationSystem

FileFile

File

File


Conventional Files• First attempts at computerised storage of records

followed traditional paper-based metaphors (“Flat file” systems)

• Flat files were inefficient for data retrieval: it might be necessary to search entire file for a record (which, it may transpire, does not exist). Remedy: index files

• Indexing improved data retrieval, but conventional files have other disadvantages: Program-Data dependence

Proprietary file formats (closed systems)

Poor scalability


Conventional Files• Disadvantages (Cont’d):

Duplicated and redundant data• ambiguity: same thing being referred to by different names in different

places• inconsistency: conflicting / unsynchronised data• wasted effort

Separation and isolation of data• data dispersed amongst many files complicates processing

Inflexibility• cumbersome data structures and report layouts• not responsive to ad hoc queries• excessive program maintenance

Development environment• procedural -v- non-procedural (3GL -v- 4GL)


Conventional Files• Advantages:

Historically, conventional files have been faster to process than DBMS applications

• As legacy file-based systems become candidates for reengineering, the trend is to replace them with database systems


Database Management Systems

• A database is a large, integrated collection of data which models a real-world enterprise

• A Database Management System (DBMS) is a software package designed to store and manage databases

• In a DBMS environment, applications are built around an integrated adaptable database

Information System

Database

Information System

Information System

Information System


• Advantages: ability to share the same data across multiple applications and

systems

data independence

control of redundancy

enforced data integrity

improved data security

uniform data administration

concurrent access

improved backup and recovery facility

flexible data structures




• Advantages (Cont’d): databases allow the use of the data in ways not

originally specified by the end-users (ad hoc queries)

database definition can be extended without impacting existing programs that use it

economies of scale


• Disadvantages: database technology is more complex than file

technology• requires more sophisticated hardware and software (DBMS)

DBMS’s can still be slower than file-based systems

database technology requires a significant investment• database administration• operating costs and ongoing maintenance• end-user training

higher impact of system failure




• Roles in a DBMS environment Data Administrator

Database Administrator

Database Designer

Application Programmer

End-User


Database Architectures

• Hierarchical Data Model

• Network Data Model

• Relational Data Model

• Object-Relational Model


Data Relationships• One-to-One (1:1)

Example: Bank Manager manages one and only one Bank Branch; Bank Branch is managed by one and only one Bank Manager

• One-to-Many (1:M) Example: An Employee works in a designated

Department; in any Department, there may be many Employees

• Many-to-Many (M:M) Example: A Student registers for one or more

Courses; for any Course, there may be one or more registered Students


Hierarchical Data Model



• Arose as a solution to a practical problem Managing millions of parts for the space program

(standard “Bill of Materials” problem)

• The basic structure is a hierarchy or tree Parent-child relationship

• Relationships are represented by pointers

• Restrictions: Each segment has at most one parent

All relationships are 1:M



• Problem: how to represent a M:M relationship ? A hierarchical structure (tree) can only support

1:M relationships. Therefore, to represent M:M, must create multiple hierarchies

… but, this means that records are duplicated in different hierarchies

duplication gives rise to data anomalies

duplication can be eliminated using pointers; must decide in which table to store data, and in which table to store pointer. This is a very awkward means of implementing M:M


Network Data Model


Network Data Model• Objective was to overcome shortcomings of the

hierarchical data model, in particular, representation of M:M relationship

• Like the hierarchical model, it can be likened to trees; unlike hierarchical model, several trees can share branches

• In practice, enjoyed little commercial success Too complex: suited to use by programmers as

opposed to end-users

Overtaken by the relational model

No clear theoretical base


Network Model: Structures

• Data item a field or attribute

• Record a collection of data items

• Vectors (repeating structures) are permitted

• Relationships are represented by sets

• Sets have owners (parent) and members (children)

• A member cannot have two parents in the one set cannot directly represent M:M relationships

• Member records of a set can be ordered


Shortcomings of Early Models

• Languages of both hierarchical and network are procedural and record-at-a-time

• To retrieve data … you must navigate (find a path) to the required

record

issue multiple statements directing the system to traverse that path

• It was necessary to issue multiple requests to the DBMS to retrieve a data item

• It was necessary to have a detailed understanding of how data was stored and structured this is contrary to data independence principle


Relational Data Model• Relational model, developed by E. F. Codd, has a strong

theoretical basis and overcomes shortcomings of network / hierarchical models

• Relational model is non-procedural (What?, not How?), and set-at-a-time

• Navigation is automatic• Relations

2-dimensional data set consisting of N columns (fields / attributes) and M rows (records)

• All rows in a relation are unique• A relational database is a set of relations


Relational Model: Structures



• Primary key A unique identifier of a row in a relation; can be

composite

• Candidate key An attribute that could be a primary key

• Alternative key A candidate key that is not selected as the primary key

• Foreign key An attribute of a relation that is the primary key of

another relation; can be composite


Relational Algebra and SQL

• A major strength of the relational model is that it supports SQL, a flexible data retrieval language which facilitates ad hoc queries

• Relational algebra is a standard for judging a data retrieval language

Relational Algebra SQL

Restrict

ProjectProduct

Union

Difference

SELECT * FROM AWHERE condition

SELECT X FROM A

SELECT * FROM A, B

SELECT * FROM A UNIONSELECT * FROM B

SELECT * FROM AWHERE NOT EXISTS (SELECT * FROM B WHERE A.X = B.X AND A.Y=B.Y AND ...

A where condition

A times B

A union B

A minus B

A [X]


Relational Databases• A truly relational database supports

structures (domains and relations)

integrity rules

a manipulation language

• The word “relational” is sometimes used too freely; many commercial systems are not fully relational because they do not support domains and integrity rules

• E. F. Codd has set forward 12 rules that a database must satisfy before it can be said to be truly relational


Codd’s 12 Rules• Information representation

All data, including metadata (data definition, constraints, user names, etc.), is represented solely and explicitly as values in a table

• Guaranteed access Every value in a database is accessed by specifying a

combination of table name, column name, and value of the primary key of the row in which it is stored

No artificial paths, such as linked lists

• Systematic treatment of null values There must be a distinct representation for unknown data,

irrespective of data type

Null values are not equivalent to zero or the empty string


Codd’s 12 Rules• Dynamic on-line catalog based on the relational model

database description is represented at the logical level in the same way as ordinary data

thus, only one manipulation language needs to be learned

• Comprehensive data sublanguage rule A relational system may support several languages and

various modes of terminal use

However, there must be at least one language that supports data definition, data manipulation, security and integrity constraints, and transaction processing operations

• View updating If the base tables of a view are updated, then the view itself

should be updated


Codd’s 12 Rules• High-level insert, update, and delete

The system must support set-at-a-time operations; for example, a set of rows can be deleted by a single statement

• Physical data independence Changes to storage representation or access methods will

not affect application programs

• Logical data independence Implementation of changes to base tables will not affect

application programs; for example, if a table is restructured or split, applications should be immune to change (views are beneficial here)


Codd’s 12 Rules• Integrity independence

Integrity constraints should be part of a database's definition rather than embedded within application programs

It must be possible to change integrity constraints without affecting any existing application programs

• Distribution independence Introduction of a distributed DBMS or redistributing existing

distributed data should have no impact on existing applications

• Nonsubversion It must not be possible to use low-level record-at-a-time

interface to by-pass high-level set-at-a-time security or integrity constraints


Codd’s Rule 0• A relational DBMS must be able to manage

databases entirely through its relational capacities

• A DBMS is either totally relational or it is not relational


Tasks in the seminars 1 - 3

• In order to do MMDB you need to be able to use SQL – self study sessions with tutor support

© michael lang, national university of ireland, galway 1 introduction to database systems

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