IT Theory
Chapter 1: Database management
Unit 1.1 : database collection
● Forms: online interactive page that allows for user input.
● Tags : Electronic tags transmit a radio frequency from the tag to a tag reader. It transmits the
information to a computer program associated with the tag.
- RFID (radio frequency identification) any chip that can store a few kilobytes of
information for data collection.
- RFID’s can be used to tag: all products stored in a warehouse
Tools stored in a workshop
The tickets of people going to conferences
Tickets of people using transport
All products sold in a shop
● Digital sensors : an electronic or electrochemical sensor, where data conversion and data
transmission are done digitally. E.g. temperature, distance, humidity, light.
● Invisible Online data collection: websites that collect data from user created content and the
storing of user personal information, email, passwords etc.
● Transaction tracking: store’s information about the transaction e.g. Where, when. Tracks
previous data and provides user safety, fraud detection. Disadvantage is the unknown tracking of
personal information.
● Location Based Data : data that provides information about different things that can be shown
on maps. Static location-based data is the tracking of stationary objects e.g. roads and buildings.
Non-static location-based information includes the position of mobile objects. E.g. planes, cars,
people.
Unit 2.2 : Data warehousing
● Data Warehousing: A technique used for storing data from more than one database.
● Data warehouses do not contain copies of all the databases but rather identified as a new
database that is created to hold structured data from multiple sources.
● Data Warehousing stores a large quantity of historical data and all the data from the various
databases are available, whereas databases store current transactions and enable easy access to
specific transactions for ongoing business processes.
Unit 2.3 : Data Mining
● Data mining: Is a process used to identify trends and patterns between different sets of data in
large databases. Helps show trends and patterns between data sets.
, ● Data mining is used to make educated guesses on the behavior of something due to its historical
trends.
Unit 2.4 : caring for and managing data
● How to protect data:
- Data validation: refers to the process in which you check whether the data is accurate,
in the correct format or of the correct type before allowing your database to record it.
- Data verification: is a manual technique that can be used to make sure that the data on
a database is correct and accurate. Two main ways: 1. Full verification which requires
that each piece of data that is entered into a database is read and checked by someone
or 2. Sample verification which randomly selects samples of data to ensure there are no
systematic errors.
- Data Integrity: The reliability, accuracy and how trustworthy data is over its entire
lifecycle.
- Logging Changes: Logging is the process of recording any changes made by users to a
database.
- Data Warehousing: can help make incorrect data more visible, allowing the problem to
be fixed.
Make the loss of data more visible, allowing the problem to be fixed.
Can be used to recover critical data if it is deleted or corrupted.
Chapter 2: Database design concepts
Unit 2.1 : characteristics of a good database
Characteristics of a good database:
● Strong enough to store all the relevant data and requirements.
● Should be able to relate the tables in the database in terms of relation.
● Multiple users should be able to access the database without affecting the other user.
● A database should be able to provide different views to different users depending on their roles.
● Data Integrity - refers to how accurate and consistent the data in a database is.
● Data Independence - refers to the separation between data and the application in which it is
being used. This allows you to update the data in your application without having to recompile
the entire application.
● Data Redundancy - refers to having the exact same data at different places in the database.
● Data Security - refers to how well the data in the database is protected from crashes, hacks and
accidental deletion.
, ● Data maintenance - refers to monthly, daily or hourly tasks that are run to fix errors within a
database and prevent anomalies from occurring.
People who are involved with running a good database:
● Developers: design and develop the database to suit the needs of an enterprise.
● Administrator: Checks the database for its usages, provides access to other users, provides any
other maintenance work required to keep the database up to date.
● End User: uses the database.
Unit 2.2: Problems with Databases
Types of problems that can occur in databases:
● Insertion anomaly: The database has been created in such a way that required data cannot be
added unless another piece of data that is unavailable is added.
● Deletion anomaly: The legitimate deletion of a record of data can cause the deletion of some
required data.
● Modification anomaly: Incorrect data may have to be changed, which could involve many
records having to be changed, leading to the possibility of some changes being made incorrectly.
Unit 2.3: How to get rid of Anomalies
Normalisation: a systematic approach of decomposing tables to eliminate data redundancy and
Insertion, Modification and Deletion Anomalies.
Goals to prevent anomalies:
● Remove all redundant data from the database.
● Remove undesirable insertions, updates and deletion dependencies.
● Reduce the need to restructure the entire database every time new fields are added to it.
● Make the relationships between tables more useful and understandable.
Types of Key Fields include:
● Primary key: the field selected by the database creator to uniquely identify each record on a
table.
● Alternative key: a field containing unique values that could be used as the primary key but is not
currently set as the primary key.
● Foreign key: a field containing values from a different table’s primary key field. Foreign keys are
used to show the relationship between different tables.
● Composite key: a combination of more than one field that uniquely identifies each record on a
table.
Chapter 1: Database management
Unit 1.1 : database collection
● Forms: online interactive page that allows for user input.
● Tags : Electronic tags transmit a radio frequency from the tag to a tag reader. It transmits the
information to a computer program associated with the tag.
- RFID (radio frequency identification) any chip that can store a few kilobytes of
information for data collection.
- RFID’s can be used to tag: all products stored in a warehouse
Tools stored in a workshop
The tickets of people going to conferences
Tickets of people using transport
All products sold in a shop
● Digital sensors : an electronic or electrochemical sensor, where data conversion and data
transmission are done digitally. E.g. temperature, distance, humidity, light.
● Invisible Online data collection: websites that collect data from user created content and the
storing of user personal information, email, passwords etc.
● Transaction tracking: store’s information about the transaction e.g. Where, when. Tracks
previous data and provides user safety, fraud detection. Disadvantage is the unknown tracking of
personal information.
● Location Based Data : data that provides information about different things that can be shown
on maps. Static location-based data is the tracking of stationary objects e.g. roads and buildings.
Non-static location-based information includes the position of mobile objects. E.g. planes, cars,
people.
Unit 2.2 : Data warehousing
● Data Warehousing: A technique used for storing data from more than one database.
● Data warehouses do not contain copies of all the databases but rather identified as a new
database that is created to hold structured data from multiple sources.
● Data Warehousing stores a large quantity of historical data and all the data from the various
databases are available, whereas databases store current transactions and enable easy access to
specific transactions for ongoing business processes.
Unit 2.3 : Data Mining
● Data mining: Is a process used to identify trends and patterns between different sets of data in
large databases. Helps show trends and patterns between data sets.
, ● Data mining is used to make educated guesses on the behavior of something due to its historical
trends.
Unit 2.4 : caring for and managing data
● How to protect data:
- Data validation: refers to the process in which you check whether the data is accurate,
in the correct format or of the correct type before allowing your database to record it.
- Data verification: is a manual technique that can be used to make sure that the data on
a database is correct and accurate. Two main ways: 1. Full verification which requires
that each piece of data that is entered into a database is read and checked by someone
or 2. Sample verification which randomly selects samples of data to ensure there are no
systematic errors.
- Data Integrity: The reliability, accuracy and how trustworthy data is over its entire
lifecycle.
- Logging Changes: Logging is the process of recording any changes made by users to a
database.
- Data Warehousing: can help make incorrect data more visible, allowing the problem to
be fixed.
Make the loss of data more visible, allowing the problem to be fixed.
Can be used to recover critical data if it is deleted or corrupted.
Chapter 2: Database design concepts
Unit 2.1 : characteristics of a good database
Characteristics of a good database:
● Strong enough to store all the relevant data and requirements.
● Should be able to relate the tables in the database in terms of relation.
● Multiple users should be able to access the database without affecting the other user.
● A database should be able to provide different views to different users depending on their roles.
● Data Integrity - refers to how accurate and consistent the data in a database is.
● Data Independence - refers to the separation between data and the application in which it is
being used. This allows you to update the data in your application without having to recompile
the entire application.
● Data Redundancy - refers to having the exact same data at different places in the database.
● Data Security - refers to how well the data in the database is protected from crashes, hacks and
accidental deletion.
, ● Data maintenance - refers to monthly, daily or hourly tasks that are run to fix errors within a
database and prevent anomalies from occurring.
People who are involved with running a good database:
● Developers: design and develop the database to suit the needs of an enterprise.
● Administrator: Checks the database for its usages, provides access to other users, provides any
other maintenance work required to keep the database up to date.
● End User: uses the database.
Unit 2.2: Problems with Databases
Types of problems that can occur in databases:
● Insertion anomaly: The database has been created in such a way that required data cannot be
added unless another piece of data that is unavailable is added.
● Deletion anomaly: The legitimate deletion of a record of data can cause the deletion of some
required data.
● Modification anomaly: Incorrect data may have to be changed, which could involve many
records having to be changed, leading to the possibility of some changes being made incorrectly.
Unit 2.3: How to get rid of Anomalies
Normalisation: a systematic approach of decomposing tables to eliminate data redundancy and
Insertion, Modification and Deletion Anomalies.
Goals to prevent anomalies:
● Remove all redundant data from the database.
● Remove undesirable insertions, updates and deletion dependencies.
● Reduce the need to restructure the entire database every time new fields are added to it.
● Make the relationships between tables more useful and understandable.
Types of Key Fields include:
● Primary key: the field selected by the database creator to uniquely identify each record on a
table.
● Alternative key: a field containing unique values that could be used as the primary key but is not
currently set as the primary key.
● Foreign key: a field containing values from a different table’s primary key field. Foreign keys are
used to show the relationship between different tables.
● Composite key: a combination of more than one field that uniquely identifies each record on a
table.
