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How foreign keys, computed (derived) attributes and compound attributes are implemented in a relational data model and then a relational database as described in "Additional Terminologies for Relational Database" and "Equivalent Terminologies used by Database Programmers, Designers, and Administrators" in CS8 in Canvas

Foreign keys, computed (derived) attributes, and compound attributes are all implemented in a relational data model and database in different ways. Here's a brief overview of each: Foreign keys: In a relational data model, foreign keys are implemented as a column in one table that refers to the primary key of another table. This is typically implemented using a foreign key constraint, which ensures that the value in the foreign key column matches a value in the primary key column of the related table. In a relational database, foreign keys can be defined using a CREATE TABLE statement with a FOREIGN KEY constraint, which specifies the name of the foreign key column and the name of the primary key column in the related table. Computed (derived) attributes: In a relational data model, computed attributes are implemented as virtual columns that are calculated based on other columns in the same table. These virtual columns are not stored in the database, but are calculated on the fly when a query is executed. In a relational database, computed attributes can be defined using a CREATE TABLE statement with a computed column specification, which includes an expression that defines the calculation for the virtual column. Compound attributes: In a relational data model, compound attributes are implemented as multiple columns in a table that are used together to represent a single attribute. In a relational database, compound attributes can be defined using a CREATE TABLE statement with multiple column specifications, which specify the name and data type for each column in the compound attribute.

What simple attributes and compound attributes are, how they are different, and how compound attributes are different from multi-valued attributes as described in "Additional Terminologies for Relational Database" in CS8 in Canvas.

In database design, attributes are the characteristics or properties of an entity that are stored in a table. Attributes can be categorized into two main types: simple attributes and compound attributes. Simple attributes: Simple attributes are those that cannot be broken down into smaller components. They are also known as atomic attributes. For example, in a customer table, the attribute "customer name" would be a simple attribute because it cannot be broken down into smaller components. Compound attributes: Compound attributes are those that can be broken down into smaller components. They are also known as composite attributes. For example, in a customer table, the attribute "address" could be broken down into its components, such as street address, city, state, and zip code. The key difference between simple attributes and compound attributes is that simple attributes cannot be further broken down, whereas compound attributes can be broken down into smaller components. Compound attributes are useful when an attribute has multiple components that can be treated as separate attributes. Compound attributes are different from multi-valued attributes in that multi-valued attributes can hold multiple values for a single instance of an entity, whereas compound attributes are made up of multiple components that are stored as separate attributes. For example, a customer may have multiple phone numbers, which would be stored as a multi-valued attribute. On the other hand, the address attribute could be a compound attribute consisting of multiple separate attributes such as street, city, state, and zip code. In summary, simple attributes are indivisible and cannot be broken down, while compound attributes consist of multiple components that can be treated as separate attributes. Compound attributes are different from multi-valued attributes in that multi-valued attributes can hold multiple values for a single instance of an entity, whereas compound attributes are made up of multiple components that are stored as separate attributes.

Different types of database technologies/database management systems as described in Ch1.2 and which one you are learning mainly in this course.

Relational database management systems (RDBMS): This is the most common type of database management system, used to store data in a structured format with well-defined relationships between tables. Examples of RDBMS include MySQL, Oracle, Microsoft SQL Server, and PostgreSQL. NoSQL databases: These databases are designed to store and manage unstructured or semi-structured data, such as text, images, and videos. Examples of NoSQL databases include MongoDB, Cassandra, and Redis.

What referential integrity actions are and how they are different as described "More about Referential Integrity, Foreign Key Constraints, Null Value Constraints and other Constraints" in CS11 in Canvas, Ch3.7, and Normalization Quiz 2 in CS13 in Canvas.

Referential integrity actions are rules that govern what happens to related records in a database when a referenced record is deleted or updated. When you create a foreign key constraint between two tables in a relational database, you can specify what action should be taken when a referenced record is deleted or updated. The available referential integrity actions are: Cascade: When a referenced record is deleted or updated, all related records in other tables are also deleted or updated. This is a very powerful action and should be used with caution. Set Null: When a referenced record is deleted or updated, the foreign key columns in related records are set to null. Restrict: When a referenced record is deleted or updated, the action is prevented if there are any related records in other tables. No Action: When a referenced record is deleted or updated, the action is prevented, but no error message is generated. It's important to note that the specific referential integrity actions that are available can vary depending on the database system you are using. For example, some database systems might not support the "set null" action, while others might have additional actions not listed above. Referential integrity actions are different from other types of constraints, such as null value constraints or check constraints. A null value constraint requires that a column cannot contain null values, while a check constraint ensures that the data entered into a column meets a specified condition. These constraints are unrelated to referential integrity actions and are used to enforce data quality rules within a table, rather than between tables. Overall, referential integrity actions are an important feature of relational databases, as they help to ensure that related data is consistent and accurate. By understanding the available referential integrity actions and how to use them effectively, you can help to create a robust and reliable database system.

What unary, binary and ternary relationships are, and how they are different as described in "More about relationships" in CS5 in Canvas, and ERD Quiz 1 & ERD Quiz 2 in CS13 in Canvas

Unary Relationship: A unary relationship exists when a table is related to itself. In other words, there is only one entity involved in the relationship. For example, in a "Person" table, a "Supervisor" field could be used to indicate the person's supervisor. This would create a unary relationship between the "Person" table and itself. Binary Relationship: A binary relationship exists when two tables are related to each other. In other words, there are two entities involved in the relationship. For example, a "Customer" table might be related to an "Order" table, where each order belongs to a specific customer. This creates a binary relationship between the "Customer" and "Order" tables. Ternary Relationship: A ternary relationship exists when three tables are related to each other. In other words, there are three entities involved in the relationship. For example, a "Student" table might be related to a "Class" table and a "Teacher" table, where each student is enrolled in one or more classes taught by a specific teacher. This creates a ternary relationship between the "Student", "Class", and "Teacher" tables. The main difference between these types of relationships is the number of entities involved. In a unary relationship, there is only one entity involved. In a binary relationship, there are two entities involved. In a ternary relationship, there are three entities involved. Another difference is the number of tables involved. In a unary relationship, there is only one table involved. In a binary relationship, there are two tables involved. In a ternary relationship, there are three tables involved. Finally, the cardinality of the relationship can also differ. Unary and binary relationships typically involve one-to-one, one-to-many, or many-to-many relationships between entities. Ternary relationships can involve complex relationships, such as many-to-many-to-many, where each entity can be related to many other entities in the relationship.

What 1-1, 1-M, and M-M relationships are, how you determine them and difference among them as described in "More about relationships" in CS5 in Canvas and ERD Quiz 1 & ERD Quiz 2 in CS13 in Canvas. Several questions may be asked about this.

1-1 Relationship: A 1-1 relationship exists when one record in a table is related to only one record in another table. In other words, each record in one table has a one-to-one relationship with a record in another table. For example, a person can have only one social security number, and a social security number can belong to only one person. 1-M Relationship: A 1-M relationship exists when one record in a table can be related to many records in another table. In other words, each record in one table has a one-to-many relationship with one or more records in another table. For example, a customer can place many orders, but each order belongs to only one customer. M-M Relationship: An M-M relationship exists when many records in one table can be related to many records in another table. In other words, each record in one table has a many-to-many relationship with one or more records in another table. For example, a student can take many classes, and each class can have many students. To determine the type of relationship between two entities, you can ask the following questions: Can one record in Table A be related to only one record in Table B? If yes, the relationship is 1-1. Can one record in Table A be related to many records in Table B? If yes, the relationship is 1-M. Can many records in Table A be related to many records in Table B? If yes, the relationship is M-M.

What database, data, information, meta data, data model, and database (management) systems are, and their differences as described "About metadata" in CS2 in Canvas and in Ch1.1. A few questions may be asked about this.

A database is a collection of organized data, while data is the raw information that is stored in the database. Information is the knowledge that is derived from the data, and metadata is data that describes other data. A data model is a conceptual representation of the data, and a database management system is the software application used to create, manage, and maintain databases.

What a foreign key is and purposes of foreign keys as described in "Additional Terminologies for Relational Database" in CS8 in Canvas.

A foreign key (FK) is an attribute added to a table to implement a relationship in a relational database. A foreign key (FK) has a matching primary key (PK) in another table (the same table in the relationship is unary) to create a logical link between rows stored in two different tables. For example, Department table has Department ID as PK. Employee table has Department ID as FK. Employee #1 may have "1" in Department ID (FK). By matching this FK data value to the PK of Department table, display the department name Employee #1 works for. SELECT * FROM Department INNER JOIN Employee ON Department.DepartmentID = Employee.DepartmentID joins Department table and Employee table by matching their PK-FK. With this JOIN operations, a relational database allows you to create more complex and useful information. A foreign key is a field (or a set of fields) in a table that refers to the primary key of another table. The primary key of the referred table is called the "referenced key" of the foreign key. The purpose of a foreign key is to enforce referential integrity, which ensures that the data in the tables is consistent and accurate. Foreign keys serve several important purposes in a relational database, including: Establishing relationships between tables: By using foreign keys, you can establish relationships between tables in a database. These relationships can be one-to-one, one-to-many, or many-to-many, and they help to ensure that the data in the tables is consistent and reliable. Enforcing referential integrity: Foreign keys help to enforce referential integrity, which ensures that the data in the tables is consistent and accurate. When a foreign key is defined, the database system checks that any value in the foreign key column matches a value in the referenced primary key column. If the value in the primary key column is deleted or updated, the database system can take appropriate actions, such as cascading the change to related tables or preventing the deletion if the record is referenced by other records. Improving performance: By using foreign keys, you can improve the performance of your database queries. For example, if you want to retrieve all records from one table that have a matching record in another table, you can use a JOIN statement that combines the tables using the foreign key. Overall, foreign keys are an essential component of a well-designed relational database, and they help to ensure that the data is consistent, accurate, and reliable.

What kind of information can be produced from a single table and what kind of information requires a relationship between two tables and their examples as learned in ERD Quiz 1 in CS13 in Canvas

A single table can provide information about a specific entity or concept, and the different attributes or properties associated with it. For example, a customer table can contain information such as customer name, address, phone number, email address, and purchase history. However, there are certain types of information that require a relationship between two or more tables. These relationships are typically represented using an Entity-Relationship Diagram (ERD). Here are some examples: One-to-many relationships: A common example of a one-to-many relationship is between a customer and their orders. A customer can have many orders, but each order is associated with only one customer. In this case, the customer table and the order table would be related, with the customer table being the "one" side and the order table being the "many" side. Many-to-many relationships: In some cases, there may be a many-to-many relationship between two entities. For example, a student can take many classes, and each class can have many students. To represent this relationship, a third table called a junction table or an association table is needed to link the two entities. Hierarchical relationships: In some cases, entities may have a hierarchical relationship. For example, an organization can have multiple departments, and each department can have multiple employees. In this case, the organization table, department table, and employee table would be related, with the department table being the child of the organization table and the employee table being the child of the department table. In summary, while a single table can provide information about a specific entity, relationships between tables are necessary to represent more complex relationships between entities and to capture a wider range of information.

Advantage of database approach as described in Ch1.2 and two articles in CS2 in Canvas & "Lessons Learned from CS2 Exercise" in CS3 in Canvas.

Data integration: A database allows for the integration of data from multiple sources, making it easier to access and manage large amounts of data. This can help reduce duplication and inconsistency in the data. Data sharing: A database can be shared among multiple users, allowing for collaboration and efficient sharing of information. This can improve communication and coordination among teams. Data consistency: With a database, data can be structured and organized in a consistent and standardized way. This helps to ensure that data is accurate and up-to-date, and reduces the risk of errors or inconsistencies. Data security: Databases have built-in security features, such as user authentication and access control, that can help protect sensitive data from unauthorized access or modification. Data backup and recovery: Databases can be backed up regularly to prevent data loss, and can also be recovered easily in the event of a system failure or other data loss incident. Scalability: A database can be scaled up or down as needed to accommodate changing data storage and management requirements, making it a flexible and adaptable solution. Overall, the database approach provides a centralized and efficient way to store and manage large amounts of data, while also providing built-in security and scalability features that can help organizations manage and protect their data effectively.

What the denormalization is, why you want to deformalize tables, what the end results of denormalization, and good candidates for denormalization as described in "About De-Normalization" in CS10 and Normalization Quiz 2 in CS13 in Canvas.

Denormalization is the process of intentionally introducing redundancy into a database design by relaxing the constraints of normalization, in order to improve the performance of queries and transactions. This is done by storing redundant copies of data in multiple tables or columns, which can simplify complex queries and reduce the need for expensive join operations. The primary goal of denormalization is to improve query performance, by reducing the number of joins and simplifying the query logic. By storing redundant data, the database can avoid performing complex and resource-intensive join operations, which can result in faster query times and improved overall system performance. The end result of denormalization is a database that is optimized for querying and transaction processing, at the expense of data redundancy and update anomalies. While denormalization can improve performance, it can also introduce the risk of inconsistent data, and can make it more difficult to maintain data integrity and consistency. Good candidates for denormalization include large and complex databases that require frequent and complex queries, and where read performance is more critical than write performance. Examples of such databases include data warehouses, decision support systems, and business intelligence systems, where the focus is on analyzing large volumes of data rather than transaction processing. In general, denormalization should be used with caution, as it can introduce potential problems with data consistency and integrity. It should only be employed after careful analysis and testing, and with a clear understanding of the trade-offs involved. A well-designed denormalization strategy should strike a balance between performance and data integrity, and should be tailored to the specific needs of the database and the applications that use it.

What supertype and subtype relationship is and examples of a supertype-subtype relationship as described in Ch2.5.

Employee supertype with subtypes of Manager and Staff: In this example, the Employee table would be the supertype and the Manager and Staff tables would be the subtypes. The Employee table would contain attributes that are common to both Managers and Staff, such as Employee ID, Name, and Hire Date. The Manager table would contain additional attributes that are specific to managers, such as Department and Budget, while the Staff table would contain attributes that are specific to staff, such as Job Title and Salary. Vehicle supertype with subtypes of Car, Truck, and Motorcycle: In this example, the Vehicle table would be the supertype and the Car, Truck, and Motorcycle tables would be the subtypes. The Vehicle table would contain attributes that are common to all vehicles, such as VIN, Make, and Model. The Car table would contain additional attributes that are specific to cars, such as Number of Doors and Fuel Type, while the Truck table would contain attributes that are specific to trucks, such as Payload Capacity and Hitch Type, and the Motorcycle table would contain attributes that are specific to motorcycles, such as Engine Size and Top Speed. Product supertype with subtypes of Book, CD, and DVD: In this example, the Product table would be the supertype and the Book, CD, and DVD tables would be the subtypes. The Product table would contain attributes that are common to all products, such as Product ID, Name, and Description. The Book table would contain additional attributes that are specific to books, such as Author and ISBN, while the CD table would contain attributes that are specific to CDs, such as Artist and Genre, and the DVD table would contain attributes that are specific to DVDs, such as Director and Rating.

How you read an ERD (Entity Relationship Diagram), especially Crows-foot symbols of ERD, relationship, relationship cardinality, the relationship minimum and maximum as described in "More about How To Read a Relationship" in CS6 in Canvas, "More about relationships" in CS5 in Canvas, and ERD Quiz 1 & ERD Quiz 2 in CS13 in Canvas. Several questions may be asked about this.

Identify the entities: The first step in reading an ERD is to identify the entities in the diagram. Entities are typically represented as rectangles. Understand the attributes: Once you have identified the entities, you can examine their attributes. Attributes are the characteristics of an entity and are usually represented as ovals. Identify the relationships: The Crows-foot symbols in an ERD are used to represent relationships between entities. The symbols are usually found at the ends of the lines connecting the entities. The relationships are usually named to indicate the type of relationship, such as "works for" or "has." Understand the relationship cardinality: The Crows-foot symbols also indicate the relationship cardinality, which specifies how many instances of one entity can be related to another entity. The symbols can be read as follows: A single line represents a 1-to-1 relationship. A short bar (or a crow's foot with three tips) represents a 1-to-many relationship. A crow's foot with two tips represents a many-to-many relationship. Understand the relationship minimum and maximum: The relationship minimum and maximum are also indicated by the Crows-foot symbols. The minimum indicates the minimum number of instances of one entity that must be related to another entity. The maximum indicates the maximum number of instances of one entity that can be related to another entity. The possible values are:

What you see in a relational data model (and thus in a relational database) and what can NOT be in a relational data model (and thus in a relational database) as descried in "Equivalent Terminologies used by Database Programmers, Designers, and Administrators", "Additional Terminologies for Relational Database", & "How to transform a conceptual data model to a logical RELATIONAL data model (by adding FKs)" in CS8 in Canvas

In a relational data model (and a relational database), you see the following elements: Tables: Tables represent the entities in the system, and each table has a set of columns that represent the attributes of the entity. Each table also has a primary key that uniquely identifies each record in the table. Columns: Columns represent the attributes of the entities and contain data values. Each column has a data type that determines the type of data that can be stored in the column. Rows: Rows represent the individual records in the table, and each row contains a set of values that correspond to the attributes defined in the columns. Relationships: Relationships represent the connections between entities in the system, and are typically defined using foreign keys. Relationships can be one-to-one, one-to-many, or many-to-many. Constraints: Constraints define rules that ensure the integrity and consistency of the data in the database. Examples of constraints include primary key constraints, foreign key constraints, and check constraints. Indexes: Indexes are used to improve the performance of database queries by allowing the database system to quickly find records based on the values in certain columns. However, there are certain things that cannot be represented in a relational data model (and thus in a relational database), such as: Complex data types: A relational database is designed to work with simple, atomic data types, such as strings, integers, and dates. It cannot handle complex data types, such as images, videos, or audio files, which would require a different type of database system. Hierarchical data: A relational database is not well-suited for storing hierarchical data structures, such as trees or graphs, which require a different type of database system. Semantic relationships: A relational database cannot easily represent semantic relationships between entities, such as the meaning of words or concepts. This type of information is better represented using a knowledge graph or semantic database. Unstructured data: A relational database is not designed to handle unstructured data, such as text documents or web pages. For this type of data, a document-oriented database or search engine is a better choice. Overall, a relational data model is a powerful tool for representing structured data and defining relationships between entities in a system. However, it is not the right solution for all types of data and may require other database systems to handle different types of data and relationships.

What the NULL value is and represents, how it is different from other data values, and how it is used in a relational database as described in Ch3.8 and Normalization Quiz 2 in CS13 in Canvas.

In a relational database, a NULL value represents the absence of a value in a particular field or column of a table. It is different from other data values because it does not represent a specific data type or value. Instead, it is a placeholder that indicates the lack of any particular value. When a field or column in a table has a NULL value, it means that there is no data in that field for that particular record. This can occur when data is missing, unknown, or not applicable to that particular record. For example, if you have a table of customers and one of the customers did not provide a phone number, the phone number field for that record would have a NULL value. NULL values are used in a relational database to indicate the absence of a value or to represent missing data. They can be used in SQL queries to filter data based on the presence or absence of data in a particular field. For example, you can use the IS NULL or IS NOT NULL operators to filter data based on the presence or absence of NULL values. It is important to note that NULL values can introduce complexity into database queries and data analysis. Because NULL values are not a specific data type or value, they can sometimes cause unexpected behavior or errors when working with data. As a result, it is important to handle NULL values carefully and to use them only when necessary.

Which normal form can be in a relational database as descried in "More about Normalization" in CS9 and Normalization Quiz 1 & Normalization Quiz 2 in CS13 in Canvas. Several questions may be asked about this

In a relational database, tables can be in any of the normal forms, ranging from 1NF to the highest normal form that satisfies the specific requirements of the database design. The choice of normal form for a given table depends on the specific needs of the database and the data being stored. As a general rule, it is considered best practice to normalize tables to at least the third normal form (3NF), as this helps to ensure that the data is well-structured, consistent, and free from redundancy. However, there may be cases where it is not necessary or practical to normalize a table to 3NF, and a lower normal form may be sufficient. In general, the higher the normal form of a table, the more complex the database design will be, as it will involve more tables and relationships between them. However, a well-designed database should strike a balance between normalization and simplicity, in order to optimize data management, querying, and performance.

What composite primary key is and where it is often used as described in "Additional Terminologies for Relational Database" in CS8 in Canvas.

In database design, a composite primary key is a primary key that consists of two or more columns in a table. A composite primary key is used when a single column cannot uniquely identify a record in a table, but the combination of two or more columns can. For example, consider a table that stores information about student grades in a course. The table may have columns for "student ID", "course ID", and "semester", with the grade for the course as the data in the table. In this case, none of the individual columns can uniquely identify a record in the table, because a student may take a course multiple times, or a course may be offered in multiple semesters. However, the combination of "student ID", "course ID", and "semester" can uniquely identify a record in the table. Composite primary keys are often used in tables that involve many-to-many relationships, where a record in one table can be linked to multiple records in another table, and vice versa. They are also used in tables where there are complex business rules that require a combination of columns to uniquely identify a record. It's worth noting that composite primary keys can add complexity to database design, and they may require additional effort to maintain data integrity. In some cases, it may be possible to use a surrogate key (a unique identifier generated by the system, such as a sequence number) as the primary key instead of a composite key, which can simplify the design and maintenance of the database.

What the computable/derived attribute is as described in "Additional Terminologies for Relational Database" in CS8 in Canvas.

In database design, a computable or derived attribute is an attribute that is not stored directly in the table, but instead is computed or derived from other attributes in the table. For example, consider a table of products, which includes attributes such as product ID, name, description, price, and quantity in stock. If we want to calculate the total value of a product in stock, we can create a derived attribute called "total value" that is calculated by multiplying the price of the product by the quantity in stock. This "total value" attribute is not stored directly in the table, but is instead computed from the values of the "price" and "quantity in stock" attributes. Derived attributes are useful because they can provide a way to calculate or derive values that are not stored directly in the table. This can be useful for reporting or analysis purposes. However, it's important to note that derived attributes may not be appropriate for all situations. They may introduce additional complexity into the database design, and there may be performance implications if they need to be calculated frequently. It's also important to note that derived attributes should not be confused with virtual attributes, which are attributes that do not exist in the table at all, but are instead generated on-the-fly through a query or some other programmatic means. Derived attributes, on the other hand, are based on the values of other attributes that do exist in the table, and are typically calculated at the time the record is created or updated.

How many relationships a table (entity type) can have with other tables (entity types) or with itself as described in "More about relationships" in CS5 in Canvas, and ERD Quiz 1 & ERD Quiz 2 in CS13 in Canvas.

In database design, a table (entity type) can have any number of relationships with other tables (entity types) or with itself. The number of relationships a table can have depends on the specific requirements of the system being designed. It is common for tables in a database to have multiple relationships with other tables. For example, a "Customer" table might have relationships with an "Order" table, a "Payment" table, and a "Contact" table. These relationships would allow the system to track information about customer orders, payments, and contact information. It is also possible for a table to have relationships with itself, known as self-referential relationships. This occurs when the same table is used to represent two different types of entities that are related to each other. For example, in a "Person" table, a "Supervisor" field could be used to indicate the person's supervisor. This would create a self-referential relationship between the "Person" table and itself. In general, the number of relationships a table can have is limited only by the complexity of the system being designed and the capabilities of the database management system being used. However, it is important to carefully consider the design of relationships to ensure that they are appropriate for the system's requirements and that they can be efficiently managed by the database management system.

What candidate keys and primary keys are. How they are different. How they are different from regular attributes and foreign keys as described in "Additional Terminologies for Relational Database" in CS8 in Canvas.

In database design, candidate keys and primary keys are used to uniquely identify a record or tuple in a table. Here's how they differ: Candidate keys: A candidate key is a column or set of columns in a table that can uniquely identify each row in the table. A table may have more than one candidate key, and each candidate key must be unique and non-null. For example, in a customer table, the candidate keys could be "customer ID" or "email address". Primary key: A primary key is a candidate key that is chosen to be the main identifier for a table. It is a unique identifier that is used to enforce data integrity and relationships with other tables in the database. A primary key cannot be null, and it must be unique across all rows in the table. For example, in a customer table, the "customer ID" column could be chosen as the primary key. The main difference between candidate keys and primary keys is that a candidate key is a potential identifier for a record in a table, while a primary key is the chosen identifier that is used to enforce data integrity and relationships with other tables. Regular attributes are columns in a table that contain data or information about a record, but are not used to uniquely identify the record. For example, in a customer table, attributes like "customer name" or "customer address" would be regular attributes. Foreign keys are columns in a table that refer to the primary key of another table. They are used to establish relationships between tables in a database. For example, in a customer order table, a foreign key could be used to link each order to the customer who placed it, using the primary key of the customer table as a reference. In summary, candidate keys are potential identifiers for records in a table, while primary keys are the chosen identifier that is used to enforce data integrity and relationships with other tables. Regular attributes are columns in a table that contain data or information about a record, but are not used to uniquely identify the record. Foreign keys are columns in a table that refer to the primary key of another table and are used to establish relationships between tables in a database.

What single-valued (atomic) attributes and multi-valued attributes are and how they are different as described in "Additional Terminologies for Relational Database" in CS8 in Canvas.

In the context of database design, attributes refer to the characteristics or properties of entities that are stored in a table. There are two main types of attributes: single-valued (atomic) attributes and multi-valued attributes. Single-valued (atomic) attributes: Single-valued attributes are those that hold a single value for each instance of an entity. They are also referred to as atomic attributes. For example, in a customer table, the attribute "customer name" would be a single-valued attribute because each customer has only one name. Single-valued attributes are important because they allow for easy querying and sorting of data. Multi-valued attributes: Multi-valued attributes are those that can hold multiple values for each instance of an entity. They are used when an entity has multiple values for a given property. For example, in a customer table, the attribute "phone number" would be a multi-valued attribute because a customer can have multiple phone numbers. Multi-valued attributes are represented using a separate table that is related to the primary table through a foreign key. The key difference between single-valued attributes and multi-valued attributes is the number of values that can be stored for each instance of an entity. Single-valued attributes hold a single value, whereas multi-valued attributes hold multiple values. While single-valued attributes are simple and straightforward to use, multi-valued attributes provide a way to store more complex data structures and can help to reduce data duplication.

Definitions and meanings of 1st, 2nd and 3rd normal forms, full functional dependency, partial functional dependency, and transitive functional dependency, and how they are related to the normalization as descried in "More about Normalization" in CS9 in Canvas. Several questions may be asked about this.

Normalization is the process of organizing data in a database into a set of tables with well-defined relationships between them, in order to reduce data redundancy and improve data integrity. There are several normal forms that define the conditions that a table must meet to be considered normalized. First normal form (1NF): A table is in 1NF if all its columns hold atomic values, meaning that each column contains only one value, and that value is not further decomposable. This means that a table should not contain repeating groups or arrays of data in a single column. Second normal form (2NF): A table is in 2NF if it is in 1NF and every non-key column is fully functionally dependent on the table's primary key. This means that each non-key column must depend on the entire primary key, not just a part of it. Third normal form (3NF): A table is in 3NF if it is in 2NF and there are no transitive dependencies between non-key columns. This means that a non-key column should not depend on another non-key column. In addition to these normal forms, there are several types of functional dependencies that are used to describe the relationships between columns in a table: Full functional dependency: A full functional dependency occurs when a non-key column depends on the entire primary key of the table. In other words, if any part of the primary key is removed, the non-key column would not be uniquely identified. Partial functional dependency: A partial functional dependency occurs when a non-key column depends on only a part of the primary key. This can lead to data redundancy and inconsistencies. Transitive functional dependency: A transitive functional dependency occurs when a non-key column depends on another non-key column in the same table. This can also lead to data redundancy and inconsistencies. In order to normalize a table, it must be transformed into a series of tables that satisfy the conditions of the normal forms. This involves breaking up the original table into smaller tables that are more focused on a single concept or set of attributes, and creating relationships between these tables using primary and foreign keys. By eliminating redundancy and ensuring that each table contains only unique data, normalization can improve data integrity and make it easier to update and maintain the database over time.

Different terms used to represent basically the same things in object-oriented class diagrams, entity relationship diagram, (relational data model), relational data model, and relational database as descried in "Equivalent Terminologies used by Database Programmers, Designers, and Administrators" in CS8 in Canvas. For example, an object class in class diagrams, an entity type in entity relationship diagrams, (a relation in the relational data model), and a table in relational databases are different terms that represent basically the same thing. Method (Behavior) in Object-Oriented Class diagram does NOT exist in entity relationship diagram, (relational data model), relational data model, and relational database

The following are different terms used to represent similar concepts across different data modeling methodologies: Class/Object - In object-oriented programming, the basic unit of code is a class, which defines a blueprint for creating objects. In an entity relationship diagram (ERD), an entity represents a similar concept, which describes a set of objects that share common attributes. Table/Relation - In a relational data model, data is organized into tables, which represent collections of related information. In a relational database, a table is a set of data elements organized in rows and columns. In an ERD, a relation represents a similar concept, which describes the association between two entities. Attribute/Column/Field - In a relational data model, a column represents a specific piece of data stored in a table. In an object-oriented programming context, an attribute is a piece of data stored in an object. In an ERD, a field represents a similar concept, which is an individual data item that is stored in a table or entity. Primary key - In a relational data model, a primary key is a column or set of columns that uniquely identify each row in a table. In an ERD, a primary key is also used to uniquely identify each instance of an entity. Foreign key - In a relational data model, a foreign key is a column that references the primary key of another table. In an ERD, a foreign key is used to represent the relationship between two entities. Overall, while there may be some differences in terminology across different data modeling methodologies, the underlying concepts are often similar or even identical. Understanding these similarities and differences can help you to more easily move between different data modeling contexts and understand how different data models relate to one another.

What the purpose of normalization is and why normalizing tables to 3NF is important as descried in "More about Normalization" in CS9 in Canvas

The purpose of normalization in database design is to reduce data redundancy and inconsistencies by organizing data into multiple related tables with well-defined relationships. Normalization ensures that each table has a single, well-defined purpose and that data is stored in the most efficient and logical way possible. By reducing redundancy and improving data integrity, normalization helps to eliminate errors and inconsistencies in data, and makes it easier to update and maintain the database over time. Normalization involves a set of rules or guidelines, often referred to as normal forms, which specify the conditions that a table must meet to be considered normalized. The most commonly used normal forms are first normal form (1NF), second normal form (2NF), and third normal form (3NF). Third normal form (3NF) is important because it ensures that each column in a table depends only on the table's primary key and not on any other non-key columns. This eliminates data redundancy and inconsistencies by separating related data into separate tables that are linked by foreign keys. For example, let's say you have a table of customer orders that includes columns for both customer data and order data. This table violates the 3NF because the customer data depends on the customer, while the order data depends on the order. To normalize this table to 3NF, you would create separate tables for customer data and order data, each with their own primary key. The customer data table would include columns such as customer name and address, and the order data table would include columns such as order number and order date. These tables would be linked by a foreign key that references the customer ID in the order data table. By normalizing tables to 3NF, you can create a database that is easier to maintain, update, and query, with fewer data inconsistencies and errors. However, it is important to note that normalization is not always necessary or appropriate for every database design. In some cases, denormalization may be used to improve performance or simplify data modeling.

What normal form a table is in and why, how to normalize a table to 3NF as descried in "More about Normalization" in CS9 and Normalization Quiz 1 & Normalization Quiz 2 in CS13 in Canvas. Several questions may be asked about this.

To determine the normal form a table is in, we need to analyze its structure and the relationships between its columns. The following is a brief overview of how to identify the normal form of a table: First normal form (1NF): A table is in 1NF if each of its columns contains only atomic values, and if there are no repeating groups of data in any column. If a table has a repeating group, it is not in 1NF. Second normal form (2NF): A table is in 2NF if it is in 1NF, and if all of its non-key columns are fully dependent on the table's primary key. If a table has a non-key column that is only partially dependent on the primary key, it is not in 2NF. Third normal form (3NF): A table is in 3NF if it is in 2NF, and if there are no transitive dependencies between its non-key columns. If a table has a non-key column that is dependent on another non-key column, it is not in 3NF. To normalize a table to 3NF, we need to break it down into smaller, more focused tables, and establish relationships between those tables using primary and foreign keys. Here is a general process for normalizing a table to 3NF: Identify the primary key(s) of the original table. The primary key(s) should uniquely identify each row in the table. Identify any non-key columns that are partially dependent on the primary key. These columns should be removed from the original table and placed in a new table with the primary key that they depend on. Identify any non-key columns that are transitively dependent on other non-key columns. These columns should be removed from the original table and placed in a new table with the primary key that they depend on. Repeat steps 2 and 3 until the resulting tables are in 3NF. Create relationships between the new tables using foreign keys that reference the primary keys of the related tables. By following these steps, we can transform an original table into a set of smaller, more focused tables that are easier to manage, and that maintain data integrity and consistency.

How to add foreign key columns to transform a conceptual data model to a logical relational data model (in other words, how to implement 1-1, 1-many, many-to-many relationships of conceptual data model in a relational data model/database) as described in "How to transform a conceptual data model to a logical RELATIONAL data model (by adding FKs)" and "FK Practice - Add foreign keys" & "FK Quiz" in CS8 in Canvas. Also, study "Additional Terminologies for Relational Database", "Why is an Associative Entity (replacing a M-M relationship) is on the Many Side?", "More than one relationship b/w same tables" & "Compare conceptual data model and logical relational data model" in CS8, as well as "Reinforcement for Attributes of M-M Relationship" in CS7 and "More about Information and Data to Design a Relational Database" in CS4 in Canvas. Several questions may be asked about this.

To transform a conceptual data model into a logical relational data model, you need to identify the entities and relationships in the conceptual model and translate them into tables and foreign keys in the logical model. Here are the steps for implementing 1-1, 1-many, and many-to-many relationships in a relational database: Identify the entities: The first step is to identify the entities in the conceptual data model and create tables for them in the relational data model. Each table should have a primary key, which is a unique identifier for each record in the table. Add foreign key columns: For each relationship between entities, you need to add a foreign key column to one of the tables. The foreign key column will reference the primary key of the other table. The foreign key column should have the same data type as the primary key column in the referenced table. Implement 1-1 relationships: To implement a 1-1 relationship between two entities, you need to add a foreign key column to one of the tables that references the primary key of the other table. The foreign key column should have a unique constraint, which ensures that each record in the table has a one-to-one relationship with a record in the referenced table. Implement 1-many relationships: To implement a 1-many relationship between two entities, you need to add a foreign key column to the "many" table that references the primary key of the "one" table. The foreign key column should not have a unique constraint, which allows multiple records in the "many" table to reference the same record in the "one" table. Implement many-to-many relationships: To implement a many-to-many relationship between two entities, you need to create a third table that represents the relationship. This table should have foreign key columns that reference the primary keys of the two entities. The foreign key columns should not have unique constraints, which allows multiple records in each table to be related to multiple records in the other table. By following these steps, you can transform a conceptual data model into a logical relational data model that includes tables and foreign keys. This logical model can then be used to create a physical database that can store and retrieve data in a structured and efficient way. Regenerate response