Managing Databases

Entity Relationship Modelling

When you are working with a relational database and forming relationships, you are linking entities together. This is known as entity relationship modelling.

Entity relationship modelling

There are 3 main types of relationships that exist:
- One to one.
- One to many.
- Many to many.
We can show these relationships in entity relationship diagrams.

One to one

A one to one relationship is when one entity is linked to one other entity.

One to many

A one to many relationship is when one entity is linked to several entitities.

Many to many

A many to many relationship is when several entities are linked to several other entitities.

Managing Data

When populating a database with data, we need to consider the methods of data capture, management and exchange.

Capturing data

Capturing data is a careful consideration that should be made and selecting what will suit your purpose best.
This could be a data collection form that the user fills out and then is entered by a data operator.
Alternatively, the data operator could enter the data with the user present.

Selecting data

It is important to select what data needs to be stored.
This is determined by how important or useful the context of the data would be.
A CCTV video recording of people who are NOT committing crimes is not suitable to store.

Database management

The management of the database is handled by the database management system.
There are lots of different database management systems (DBMS), including:
- MySQL.
- Oracle.
- Bigtable.

Exchanging data

The data in a database can be exchanged in different ways.
Traditionally this data is exchanged as a comma separated value file (.csv).
Other formats used are XML and JSON.
These formats contain markup that make the data easier to read and manipulate.

Exchanging data cont.

Schools, colleges and universities make use of EDI (Electronic Data interchange).
Each organisation's system can be configured to read and write to each another's database.

Referential integrity

Referential integrity is a key principle for relational databases.
Referential integrity helps to ensure no data redundancy and inaccuracies between linked tables.
Referential integrity prevents a record containing a foreign key from using a value that doesn’t exist in the linked table where it is a primary key.

Normalisation to 3NF

Normalising data is a process which makes a database more efficient to run and store.

Stages of normalisation

To ensure the most efficient database structure possible, we use a process called normalisation.
There are 3 stages of normalisation:
- First normal form (1NF).
- Second normal form (2NF).
- Third normal form (3NF).
Normalisation can turn a flat-file database into a relational database.

Unnormalised data

Before going to 1NF, a flat-file table structure is put together.
We refer to this stage as unnormalised data or 0NF.

First normal form

For a database to be in 1NF it has to obey the following:
- No columns with repeated or similar data.
- Each row is unique with a primary key.
- Each field must have a unique name.

Second normal form

For a database to be in 2NF, it has to be in 1NF.
Each of the fields must rely on the primary key to be unique.

Third normal form

For a database to be in 3NF, it has to be in 2NF.
No field should rely on another non-key field.
For example, a database should not store someone's age and date of birth.
It should store the date of birth only and the surrounding software should be used to calculate the age.

1Components of a Computer

1.1Structure & Function of the Processor

1.1.1Structure of the CPU

1.1.2Fetch, Decode, Execute Cycle

1.1.3Factors Affecting CPU Performance

1.1.4CPU Architecture

1.2Types of Processors

1.2.1CISC & RISC

1.2.2Multicore & Parallel Cores

1.3Input, Output & Storage

1.3.1Computer Systems

1.3.2Magnetic Storage

1.3.3Optical Storage

1.3.4Types of Optical Storage

1.3.5Types of Memory

1.3.6Flash Memory

1.3.7Virtual Memory

2Software & Software Development

3Exchanging Data

4Data Types, Data Structures & Algorithms

4.1Data Types

4.1.1Data Types

4.1.2Arrays & Strings

4.1.3Representing Numbers

4.1.4Binary Arithmetic

4.1.5Hexadecimal

4.1.6Converting Binary Numbers

4.1.7Floating Points in Binary

4.1.8Floating Point Arithmetic

4.1.9Bitwise Manipulation

4.1.10Character Sets

4.2Data Structures

4.2.1Arrays, Linked Lists & Queues

4.2.2Graphs, Hash Tables & Stacks

4.2.3Trees

4.3Boolean Algebra

4.3.1Computational Logic

4.3.2Boolean Logic

4.3.3Karnaugh Maps

4.3.4Boolean Identities

4.3.5Flip Flops & Adder Circuits

5Legal, Moral, Cultural & Ethical Issues

5.1Computing Related Legislation

5.1.1Data Protection Act (1998)

5.1.2Computer Misuse Act (1990)

5.1.3Copyright Designs and Patents Act (1988)

5.1.4Regulation of Investigatory Powers Act (2000)

5.2Moral & Ethical Issues

5.2.1Online Activity Tracking

5.2.2Censorship

5.2.3Cultural Issues

5.2.4Environmental Issues

5.2.5Layout, Colour Paradigms & Character Sets

6Elements of Computational Thinking

6.1Thinking Abstractly

6.1.1Abstraction

6.2Thinking Procedurally

6.2.1Decomposition

6.3Thinking Logically

6.3.1Pseudocode & Flow Diagrams

7Problem Solving & Programming

7.1Programming Techniques

7.1.1Programming Fundamentals

7.1.2Modularity

7.2Programming Construction

7.2.1Integrated Development Environments

7.2.2Testing

8Algorithms

8.1Algorithms

8.1.1Interpreting, Correcting & Completing Algorithm

8.1.2Sort Algorithms

8.1.3Insertion & Merge Sort

8.1.4Search Algorithms

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