Please see attachment for instructions.
WK5 Discussion Instructions: DML Anomalies and Functional Dependencies
250 words total, answer the questions below with 4 evidence base scholarly articles. APA format, due 15 Nov 24.
1. Discuss insertion, deletion, and modification anomalies. Why are they considered bad? Illustrate with examples.
2. What is meant by the closure of a set of functional dependencies?
400 words total, replying to the two posts below. Each reply must be 200 words for post 1 and post 2.
Bowen Post #1
Hello Class,
For this week’s discussion we were asked to discuss insertion, deletion, and modification anomalies and why they are considered bad. Well according to our reading, an insertion anomaly occurs when certain attributes cannot be inserted into the database without other existing attributes. Deletion anomaly occurs when deleting information inadvertently causes other information to be lost too. Modification anomaly occurs when changes have to be made in multiple places due to a single logical change. All of these anomalies are considered bad because they can disrupt data integrity, consistency, and even lead to system crashes. Which in turn can cause bad decision making due to unreliable data (Elmasri & Navathe, 2015).
Ramez Elmasri and Shamkant Navathe provide several examples of each anomaly in our reading. An example of insertion anomaly would be a student database where a student is enrolled without any registered courses. The example they provided for deletion anomaly is a customer’s data being deleted from an order database and all other important information regarding that customer being deleted as well. Lastly, the example that the authors provided for modification anomaly is an employee’s address changes in a database causing it to need to be updated in multiple other records as well. All of these instances can lead to inconsistencies which in turn can be harmful for any organization (Elmasri & Navathe, 2015).
We were also asked to define the closure of a set of functional dependencies. According to our reading, the closure of a set of functional dependencies are dependencies that can be implied from an original set of data. The example given in the book is a classroom database. In this database if each student has a unique ID, then the student ID functionally determines the student’s name, as each ID is associated with exactly one name (Elmasri & Navathe, 2015).
References
Elmasri, Ramez, and Sham Navathe.
Fundamentals of Database Systems. 4th ed., Pearson/Addison Wesley, 2004.
Narro Post #2:
Week 5, Hello Classmates:
· Discuss insertion, deletion, and modification anomalies. Why are they considered bad? Illustrate with examples.
An insertion anomaly happens when certain data cannot be inserted without the presence of additional, unrelated data. Example used in our text: an employee starting a position within the company but doesn’t have the assigned department, so a NULL value is placed. By placing NULL values can lead to incomplete data being stored, which may compromise data integrity. Deletion anomalies is stated to be related to insertion anomaly situations when deletion of a single piece of data inadvertently can result in the loss of addition unintended data. For example, if the employee was to leave and the record was deleted, the information concerning the department is lost inadvertently from the database. Modification anomalies occur when changes are made to vales of the attributes, for example changing the manager in a department all employees with that manager must be updated. Modifying data can lead to inconsistencies and increase of maintenance complexity.
· What is meant by the closure of a set of functional dependencies?
Useful to define the concept Closure formally which includes all possible dependencies that can be inferred from the given set F. the set of dependencies F+, called the closure of F, can be determined from F by using only inference rules IR1 THROUGH IR3. With schema design, understanding the closure, ensures the database design reduces redundancy and maintains data integrity. Knowing all possible dependencies helps optimize query performance by identifying the best ways to retrieve and join data.
