Converting ER Diagrams to Tables Flashcards

1
Q

Why is it not a simple one-to-one translation from an ER diagram (conceptual schema) to a relational table (relational schema)?

A

The translation from an ER (Entity-Relationship) diagram to a relational schema is not a simple one-to-one process because not all ER constructs can be directly translated into relational structures. The complexity arises due to differences in the representation of relationships, cardinality, and other concepts between the two models.

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2
Q

What are the primary goals of restructuring ER diagrams before translation into a relational schema?

A

The primary goals of restructuring ER diagrams before translation into a relational schema are to reduce redundancy and improve efficiency. This involves refining the conceptual schema to eliminate ambiguities, represent relationships clearly, and ensure that the resulting relational schema is well-organized and efficient.

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3
Q

What are the typical steps involved in the process of translating ER diagrams into relational schemas?

A

The typical steps in translating ER diagrams into relational schemas include:

Restructure ER Diagrams: Refine the ER diagram, resolve any ambiguities, and ensure clarity in representing entities, relationships, and attributes.
Translation into Relational Schema: Map entities, relationships, and attributes from the ER diagram to tables, keys, and constraints in a relational database schema. This involves creating tables, defining primary and foreign keys, and establishing relationships.

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4
Q

What is the recommended approach for handling multi-value attributes in an entity within a relational database design?

A

Ideally, multi-value attributes should be removed from the entity and split into a separate relation, especially when aiming for normalization and a more structured database design. This involves creating a new table (relation) to represent the multi-valued attributes and establishing a relationship between the original entity and the new relation.

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5
Q

Why is it beneficial to split multi-value attributes into a separate relation and a weak entity?

A

Splitting multi-value attributes into a separate relation and a weak entity is beneficial for several reasons:

Normalization: It supports the normalization process, reducing redundancy and improving the overall structure of the database.
Maintainability: It enhances the maintainability of the database by organizing data more systematically.
Flexibility: It allows for a more flexible and extensible design, making it easier to adapt to changes in requirements.

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6
Q

What is the role of the new relation when handling multi-value attributes?

A

The new relation represents the multi-value attributes and establishes a relationship with the original entity. This relationship typically involves using foreign keys to link the instances of the original entity to the corresponding instances in the new relation.

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7
Q

How can a weak entity be defined in the context of a relational database?

A

A weak entity is an entity that depends on the existence of another entity. Unlike regular entities, weak entities often do not have a candidate key of their own and rely on the primary key of a related parent entity.

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8
Q

What is a common characteristic of the primary key for a weak entity?

A

The primary key for a weak entity is typically derived from the primary key of its parent entity. It uses the primary key of the parent entity as part of its own key to establish a dependency.

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9
Q

What are some common reasons for the existence of weak entities?

A

Weak entities usually result from conscious choices by data modelers or when there is no global authority capable of creating a unique identifier. They are designed to represent entities that cannot exist independently and are inherently tied to the existence of another entity.

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10
Q

How should one approach handling repeating groups in an entity within a relational database design?

A

When dealing with repeating groups in an entity, it’s essential to be cautious as these multi-value attributes may be logically related. For instance, considering a scenario where, on each visit, a patient sees one doctor per day and only consults one doctor about one symptom, assumptions like these should be carefully considered.

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11
Q

What are potential issues with repeating groups, and how can they be addressed?

A

Potential issues with repeating groups include redundancy and difficulty in maintaining data integrity. To address these issues, it’s advisable to create separate entities and relationships. In the example given, creating a Doctor entity and establishing relationships based on assumptions can lead to a more structured and normalized database design.

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12
Q

Why might it be beneficial to create a separate entity (e.g., Doctor) and establish more relationships in the case of repeating groups?

A

Creating a separate entity, such as a Doctor entity, and establishing more relationships allows for a more flexible and normalized database design. It helps avoid redundancy and ensures that relationships between entities are well-defined, making it easier to query and maintain the database.

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13
Q

What conditions should an entity satisfy in a relational database model?

A

An entity in a relational database model should satisfy at least one of the following conditions:

It has at least one non-key attribute.
It is the “many” side in a many-one or many-many relationship.

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14
Q

What are some rules of thumb to distinguish between entities and attributes?

A

If something is a “thing” in its own right, it’s likely an entity (e.g., patient).
If something is a “detail” about some other “thing,” it’s likely an attribute (e.g., name).
If something is a “detail” correlated among many “things,” it’s likely an entity (e.g., patient history).

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15
Q

What is the underlying principle behind distinguishing between entities and attributes?

A

The distinction between entities and attributes is largely about avoiding redundancy. Entities represent distinct “things” with their own attributes, while attributes are details about those entities. This distinction helps ensure a well-organized and normalized database design.

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16
Q

What are the design choices when modeling concepts in the real world in a database?

A

When modeling concepts in the real world in a database, design choices include deciding whether a concept should be modeled as an entity, an attribute, or a relationship. These choices help structure the data representation in a way that reflects the relationships and characteristics of the real-world entities.

17
Q

What are the limitations of the Entity-Relationship (ER) model in capturing data semantics?

A

While the ER model is powerful in capturing a lot of data semantics, it has limitations in fully representing all aspects of the real world. Some nuances and complexities of real-world scenarios may not be entirely captured within the constraints of the ER model.

18
Q

What is the key to a successful database model, and what does “parsimony” refer to in this context?

A

The key to a successful database model is parsimony, which means being as complex as necessary but no more. In other words, the model should capture the essential aspects of the real world without unnecessary complexity. Choosing to represent only “relevant” things ensures that the model remains manageable and effective.

19
Q

When translating an ER diagram into a relational schema, what are the key components to consider for each entity or relationship?

A

When translating an ER diagram into a relational schema, the key components to consider for each entity or relationship include:

Attributes: Identify the attributes associated with each entity or relationship and determine their data types.
Candidate Key: Define the candidate key for each table, which is a set of attributes that uniquely identifies each tuple.
Foreign Key: Establish foreign keys to represent relationships between tables, ensuring referential integrity.

20
Q

Why is it important to carefully identify the candidate key for each table during the translation process?

A

Identifying the candidate key is crucial because it serves as a unique identifier for each tuple in the table. It ensures that each row can be uniquely identified, which is essential for maintaining data integrity and avoiding duplicate entries.

21
Q

How are foreign keys used in the relational schema to represent relationships between tables?

A

Foreign keys are used to establish relationships between tables by referencing the primary key of another table. This creates a link between related tables, enforcing referential integrity and allowing for the representation of associations between entities in the database.

22
Q

How do you convert a binary relationship set R into a table in a relational schema?

A

To convert a binary relationship set R into a table, create a table with attributes that include the candidate keys of both entities A and B, foreign keys referencing A and B, and any attributes specific to R. The candidate key of the table depends on the cardinality constraint of R.

23
Q

What is the candidate key of R in the case of a many-to-many relationship?

A

In a many-to-many relationship, the candidate key of R includes all the attributes in the candidate keys of both entities A and B.

24
Q

How is the candidate key determined for a one-to-many relationship?

A

In a one-to-many relationship, the candidate key of R is the same as the entity set on the “many” side, which is entity B in this case.

25
Q

What is the candidate key structure for a one-to-one relationship?

A

In a one-to-one relationship, R has two candidate keys. The first (or second) one is the same as the candidate key of entity A (or B).

26
Q

How do you handle a multi-way relationship set R when converting it into a table?

A

For a multi-way relationship set R, create a table that includes the candidate keys (and foreign keys) of the participating entity sets, attributes of R, and the candidate key of the table, which includes all the attributes of the candidate keys of the participating entity sets.

27
Q

What aspect is not captured by the conversion process?

A

The conversion process does not automatically capture participation constraints, such as ensuring that every class is taken by at least one student. Participation constraints may be checked when necessary using SQL queries.

28
Q

How can total participation in a relationship be captured without a separate relationship table?

A

Total participation in a one-to-many relationship on the “one” side can be captured without a separate relationship table by enforcing the total participation through a foreign key in the entity itself.

29
Q

Why might having NULL values in some tuples be discouraged in the case of partial participation?

A

Having NULL values in some tuples due to partial participation may not be encouraged because it introduces potential complications and may require additional handling in queries and application logic.

30
Q

Will the schema obtained through the conversion process be considered “good”?

A

The schema obtained through the conversion process will be good in terms of ensuring there is no redundancy, but only with respect to what the ER diagram represents. The process focuses on representing entities, relationships, and their cardinalities efficiently.

31
Q

What might be missing from the schema obtained through the conversion process?

A

The schema obtained through the conversion process might lack consideration for functional dependencies. Functional dependencies involve relationships between attributes within a table, and this aspect is not explicitly captured during the ER diagram to relational schema conversion.

32
Q

Why are functional dependencies important in assessing the quality of a schema?

A

Functional dependencies are crucial in assessing the quality of a schema because they define relationships between attributes within a table. Understanding these dependencies helps in designing tables that minimize redundancy and ensure data integrity, optimizing the overall performance of the database.

33
Q

How can the absence of functional dependencies be addressed in the schema design process?

A

To address the absence of functional dependencies, additional steps beyond the ER diagram to relational schema conversion may be necessary. Analyzing the data and identifying dependencies between attributes within tables can help refine the schema and improve its overall quality.