Untitled Quiz

Accidental data loss can occur from human error, software failure, or hardware failure.
Data can be lost through theft and fraud.
Loss of privacy or confidentiality can occur, especially concerning personal and corporate data.
Loss of data integrity can also happen due to accidental or intentional actions.
Loss of data availability can occur due to sabotage.

Data security aims to protect data against accidental or intentional loss, destruction, or misuse.

Views or subschemas: Allow users to see only the specific data they need and restrict access to sensitive information.
Integrity Controls: Enforce database conditions to ensure data accuracy and consistency.
Authorization Rules: Control access to data based on users' permissions.
Triggers: Prevent inappropriate actions, invoke special procedures, and write to log files to track changes.
User-defined procedures: Define specific actions that can be performed on the database.
Encryption: Protects data by transforming it into an unreadable format.
Authentication schemes: Verify users' identities before granting access to the database.

Widely used personal productivity tools can introduce problems similar to old file systems.
Issues include:
- Ad hoc queries impossible
- Difficulty in changing existing structures.
- Security features difficult to program and often omitted in file system environments.

Structural Dependence: Access to a file depends on its structure, requiring changes to all programs when the structure changes.
Data Dependence: Data access changes when data storage characteristics change, making it difficult to access and modify information.
Data Independence: Ideally, changes in data storage characteristics should not affect data access.

Data Redundancy: Duplicate data storage in different locations.
Data Inconsistency: Conflicting versions of the same data stored in different places.
Data Anomalies: Abnormalities arise when changes in redundant data are not made correctly.
- Update Anomalies: Inconsistent data updates.
- Insertion Anomalies: Difficulty in adding new data.
- Deletion Anomalies: Loss of related data during deletion.

Data-modeling skills are crucial during the database design process.
Good data modeling facilitates communication between the designer, user, and developer.

A database system is a collection of logically related data stored in a single logical data repository.
Data might be physically distributed among multiple storage facilities.
DBMS (Database Management System): Eliminates most of the file system's problems.
- Stores data structures, relationships between structures, and access paths.
- Defines, stores, and manages all access paths and components.

Hardware: All the system's physical devices.
Software: Includes:
- Operating system software.
- DBMS software.
- Application programs and utility software.
People: All users of the database system, including:
- System and database administrators.
- Database designers.
- Systems analysts and programmers.
- End users.
Procedures: Instructions and rules that govern the design and use of the database system.

DBMS creates and manages complex structures for data storage.
It also stores related data entry forms, screen definitions, report definitions, etc.
DBMS provides data abstraction and removes structural and data dependency.

DBMS uses algorithms to enable concurrent access without compromising data integrity.

DBMS provides access to data through a query language.
SQL (Structured Query Language) is the standard query language used by most DBMS vendors.

Allow different applications to communicate with the DBMS and access the database.

Primary Key: A unique identifier for each record in a table, typically placed at the beginning.
Foreign Key: Data from one table that is related to data in another table.
Bridge Entity: Connects two tables that are not directly related.

Data models are simple representations of complex real-world data structures.
Model: An abstraction of a real-world object or event.
Data Modeling: The process of creating a specific data model for a determined problem domain. It is iterative and progressive.

Data modeling is the first step in designing a database.
They help understand the complexities of the real-world environment.### Relational Model
Created by E.F.Codd at IBM in 1970
Resembles the hierarchical model (but includes more relationships between instances)
Maintains a database standard for organization
Consists of tables (relations)
tables can be thought of as matrices
considered impractical at the time
conceptually simple but expensive
Relational Data Management System (RDBMS) improves performance and can hide complexity from the user
performs the same functions as the hierarchical model

Uses Chen Notation for visual representation
uses diamonds to represent the relationships between entities.
Relationship name is written inside the diamond.
Crow's Foot notation is a common standard for representing relationships between entities

Operates at the lowest level of abstraction (storage and access)
Defines the way data is saved on storage media
Does not require details at the physical level of abstraction
The relational model aims for logical level
Physical Independence: changes to the model do not affect the internal logical view.