IB COMPUTER SCIENCE OPTION A DATABASES

DATABASES | DATABASE MANAGEMENT

ALSO IN THIS TOPIC
BASIC CONCEPTS
RELATIONAL DATABASES 2.1 - 2.8
RELATIONAL DATABASES 2.9 - 2.20
YOU ARE HERE | DATABASE MANAGEMENT
DATABASE MODELS AND ANALYSIS
DATABASE KEY TERMINOLOGY
DATABASE ANSWERS

SECTION 1 | THE ROLE OF THE DATABASE ADMINISTRATOR

A database administrator (DBA) is responsible for the design, implementation, maintenance, and management of an organisation's databases. The specific responsibilities of a DBA may vary depending on the size and complexity of the organisation, but some common responsibilities include:

Design and implementation: DBAs are responsible for designing and implementing the database architecture, including the physical storage and organisation of the data, the logical relationships between the data entities, and the security and access controls.
Maintenance and performance tuning: DBAs are responsible for maintaining the databases and ensuring their performance and availability. This includes monitoring performance metrics, tuning the database for optimal performance, and performing regular backups and disaster recovery operations.
Data security: DBAs are responsible for ensuring the security and privacy of the data stored in the databases, including the implementation of access controls, data encryption, and other security measures.
User management: DBAs are responsible for managing user accounts and permissions, ensuring that the appropriate access controls are in place to ensure the security and privacy of the data.
Data modelling and architecture: DBAs are responsible for defining the data models and architecture that support the organisation's data requirements, ensuring that the data is organised in a way that supports the organisation's goals and objectives.
Monitoring and troubleshooting: DBAs are responsible for monitoring the databases and troubleshooting any issues that arise, including performance bottlenecks, data integrity problems, and security incidents.
Training and support: DBAs may also be responsible for providing training and support to other stakeholders, such as developers and end-users, to help them effectively use the databases and understand the data stored in the databases.

The role of a DBA is critical to the success of an organisation, as the DBA is responsible for ensuring the accuracy, security, and performance of the organisation's data. By fulfilling these responsibilities, DBAs help organisations to make informed decisions and support their goals and objectives.

SECTION 2 | HOW END USERS INTERACT WITH THE DATABASE

Database administrators, internal employees, and external customers all have different roles and responsibilities within an organisation, and as a result, they may interact with a database in different ways. Additionally, the interfaces provided to these groups may also differ, depending on their specific needs and requirements. Here are some examples of how these groups may interact with a database and the interfaces they may be provided with:

Database Administrators: Database administrators (DBAs) are responsible for managing the database and ensuring its security, availability, and performance. They typically interact with the database using specialized tools that are designed to manage and monitor databases. These tools may include command-line interfaces, GUIs, or web-based interfaces that allow DBAs to perform tasks such as creating backups, managing users and permissions, monitoring performance, and tuning the database.
Internal Employees: Internal employees may interact with a database in a variety of ways, depending on their roles and responsibilities. For example, sales representatives may use a web-based interface to access customer data and create new orders, while managers may use reporting tools to generate reports and analyse data. The interfaces provided to internal employees may be customised to their specific needs, and may include features such as forms, dashboards, and reports that are tailored to their roles.
External Customers: External customers may interact with a database using web-based interfaces or mobile applications. These interfaces may allow customers to view their account information, place orders, or track shipments. The interfaces provided to external customers are typically designed to be easy to use and intuitive, with a focus on providing a positive user experience.

Database administrators, internal employees, and external customers may interact with a database in different ways, depending on their roles and responsibilities. The interfaces provided to these groups may also differ, depending on their specific needs and requirements. The key is to provide an interface that is customised to each group, and that makes it easy to perform the tasks they need to do in an efficient and user-friendly manner.

SECTION 3 | DATABASE RECOVERY

Database recovery refers to the process of restoring a database to a consistent state after a failure or an error. There are different methods of database recovery, depending on the type of failure and and recovery time required by the organisation. Here are some different methods of database recovery:

System Log: A system log, also known as a transaction log or audit trail, is a record of all changes made to a database. It is used to track the history of database transactions, and to provide a way to recover from errors or failures. The system log records details such as the time and date of the transaction, the user who made the change, and the type of change that was made. By using a system log, it is possible to undo or redo changes that were made to the database, which can be useful in recovering from errors or restoring the database to a previous state.
Deferred Update: Deferred update is a technique used in database management to improve performance and reduce the risk of data inconsistencies. In a deferred update system, changes made to a database are not immediately written to disk, but are instead held in memory until a commit point is reached. Once the commit point is reached, all the changes are written to the database in a single batch. This can reduce the overhead of writing to the disk for each individual transaction, and can improve performance. However, it also means that data may not be immediately available for other transactions, which can lead to concurrency issues.
Mirroring: Mirroring involves creating a duplicate copy of a database on a separate server. In the event of a failure, the duplicate copy can be used to recover the database. This method can be used to provide high availability and fast recovery times, but it can be complex to set up and maintain.

Some methods that might appear on the IB mark-scheme could be System Log and Deferred Update, however there are several methods of database recovery, including backup and restore, point-in-time recovery, replication, log shipping, and mirroring. The choice of method depends on the type of failure and the resources available to implement and maintain the recovery solution.

SECTION 4 | INTEGRATED DATABASE SYSTEM

An integrated database system, also known as an integrated data management system (IDMS), is a system that provides a centralised, unified view of data from multiple sources. It is designed to integrate data from various systems, applications, and databases, and to provide a single point of access for users. Here's an outline of how integrated database systems function:

Data Collection: The first step in an integrated database system is data collection. Data is collected from various sources, such as databases, applications, and file systems. This data is then consolidated into a single database or data warehouse.
Data Integration: The next step is data integration. Data from different sources is integrated into a single, unified format. This involves transforming data from its original format into a common format that can be easily accessed and analysed.
Data Cleansing: Data cleansing involves identifying and correcting errors, inconsistencies, and duplicates in the data. This is an important step in ensuring that the data is accurate, complete, and consistent.
Data Storage: Once the data has been collected, integrated, and cleansed, it is stored in a centralised database or data warehouse. This database is optimised for querying and analysis, and may use specialised storage technologies such as columnar storage or in-memory databases.
Data Access: The final step is data access. Users can access the data in the integrated database system using a variety of tools, such as SQL queries, data visualisation tools, or reporting tools. The system may also provide APIs or web services for programmatic access.

An integrated database system is designed to provide a centralised, unified view of data from multiple sources. It involves collecting data, integrating it into a common format, cleansing it, storing it in a centralised database or data warehouse, and providing access to users through a variety of tools and interfaces. The goal is to provide a single point of access for users and to ensure that the data is accurate, complete, and consistent

SECTION 5 | EXAMPLE USE OF DATABASES

Databases are widely used in many different areas to store and manage data. Here's an outline of how databases are used in specific areas:

Stock Control: Databases are used in stock control systems to manage inventory levels and track sales. The database contains information about each product, including its SKU, description, price, and quantity on hand. When a sale is made, the database is updated to reflect the change in inventory levels. Reports can be generated from the database to help with forecasting, ordering, and reordering.
Police Records: Databases are used in police records systems to store and manage information about crimes, suspects, and victims. The database contains details such as the location and date of the crime, the type of crime, and any evidence or witness statements. This information can be used to identify patterns, track suspects, and solve crimes.
Health Records: Databases are used in healthcare systems to store and manage patient health records. The database contains information such as the patient's name, age, medical history, diagnoses, medications, and test results. This information can be used by healthcare providers to make informed decisions about treatment, monitor patient progress, and provide better care.
Employee Data: Databases are used in human resources systems to store and manage employee data. The database contains information such as the employee's name, address, contact information, job title, salary, and benefits. This information can be used to manage payroll, track performance, and provide benefits to employees.

Databases are used in many different areas to store and manage data, including stock control, police records, health records, and employee data. The information stored in these databases can be used for a variety of purposes, such as tracking inventory, solving crimes, providing healthcare, and managing human resources.

SECTION 6 | DATA PROTECTION

Ensuring the privacy of personal data is essential in any organisation that collects, stores, or processes personal information. Organisations must adhere to various data protection rules such as the Data Protection Act and Computer Misuse
Act, whilst law vary in different countries, most countries take the privacy of personal data seriously. Here are some methods to help keep data private from both a computer and human point of view:

TECHNOLOGY METHODS

Data Encryption: Data encryption is a process of converting data into a coded form to protect its confidentiality. Encryption can be used to protect data in transit or data at rest.
Access Controls: Access controls are security measures that restrict access to data based on the user's identity, role, or permissions. This can include password protection, multi-factor authentication, and role-based access control.
Secure Data Storage: Data should be stored in a secure location, such as a server room with access control and surveillance cameras. In addition, data backups should be stored offsite in a secure location.
Regular Security Audits: Regular security audits should be conducted to ensure that data is protected from unauthorised access, and that all security controls are functioning as intended.

HUMAN METHODS

Employee Training: Employees should be trained on the importance of data privacy, as well as the policies and procedures for protecting personal data. Training should include how to handle personal data securely and how to detect and report any privacy breaches.
Access Controls: Access controls should also be enforced for human users, including restricting access to personal data on a need-to-know basis, and providing training on how to handle sensitive data.
Background Checks: Employees who have access to personal data should undergo background checks to ensure that they have a trustworthy background.
Privacy Policies and Notices: Privacy policies and notices should be created and made available to all employees, customers, and partners. These should clearly state the organization's privacy practices and procedures.

Ensuring privacy of personal data from both a computer and human point of view requires a combination of technical and organizational measures. This includes data encryption, access controls, secure data storage, regular security audits, employee training, access controls for human users, background checks, and privacy policies and notices. By implementing these measures, organizations can ensure the confidentiality, integrity, and availability of personal data.

SECTION 7 | OPEN TO INTERROGATION

Third parties such as the police or medical service may need to interrogate database systems for a variety of reasons, including:

Criminal Investigations: Law enforcement agencies may need to interrogate database systems to gather evidence in criminal investigations. This may involve accessing records of suspects, victims, or witnesses.
Medical Emergencies: Medical services may need to interrogate database systems to access medical records in cases of emergency. This can help medical professionals make informed decisions about treatment and care.
Compliance and Regulations: Some industries, such as finance and healthcare, are subject to strict regulations and compliance requirements. Third parties may need to interrogate database systems to ensure that organisations are complying with these regulations.

It is important to balance the need for access with privacy and security concerns, and to ensure that access to personal data is limited to those who have a legitimate need to know.

SECTION 8 | DATA MATCHING AND DATA MINING

Data matching and data mining are two distinct techniques used in database management and analysis.

DATA MATCHING
Data matching is a process of comparing two or more datasets to identify matches or duplicates. It involves searching for records in one dataset that match records in another dataset, based on certain criteria. Data matching is typically used for data integration, fraud detection, and identity verification. For example, a bank may use data matching to identify duplicate records in its customer database or to match customer records with government records to verify customer identities.

DATA MINING
Data mining is a process of analysing large datasets to discover patterns and relationships. It involves using statistical and machine learning algorithms to analyse data and uncover insights. Data mining is typically used for business intelligence, marketing, and scientific research. For example, a retailer may use data mining to analyse customer purchase patterns and identify which products are frequently bought together.

The key difference between data matching and data mining is their focus. Data matching is focused on identifying matches or duplicates between datasets, whereas data mining is focused on discovering patterns and relationships within a single dataset. Data matching is typically used for data integration and identity verification, while data mining is typically used for business intelligence and scientific research.

Data matching is a process of comparing two or more datasets to identify matches or duplicates, while data mining is a process of analysing large datasets to discover patterns and relationships.

ALSO IN THIS TOPIC

BASIC CONCEPTS
RELATIONAL DATABASES 2.1 - 2.8
RELATIONAL DATABASES 2.9 - 2.20
DATABASE MANAGEMENT
DATABASE MODELS AND ANALYSIS
DATABASE KEY TERMINOLOGY
DATABASE ANSWERS