Give you some first hand experience converting an existing data table into a normalized design.

INTRODUCTION: You land a job with a small movie rental store. They have been managing their rental records by means of a small spreadsheet, but they realize that they have occasionally entered some conflicting information into their database. Just last week they had one record that claimed that the Movie Logan was a comedy and the rest of the records for that movie portrayed it as an action film. You ask them for some sample data, and they provide you with sample data here: Upon questioning the owner, you find:

As small as this video store is, they still have customers like Robert Phil who are two different people, but just happen to have the same name.

The video store computes the invoice cost when the customer turns in the videos. That cost depends on how long the customer has had the video. If you see no invoice cost, that means that the customer has not returned one or more of the videos. The store would like to be able to capture the date on which the customer returns each video.

The Movies Rented and the Category columns parallel each other. For each movie in the Movies Rented column, the corresponding value in the Category column represents the type of movie. For instance, since the Hunt for Red October is the third entry in that value for Movies Rented, that means that it is an Action film, since it is the third value in the corresponding Category entry. PROCEDURE: Using the process that we talked about in class, normalize the above table structure one step at a time until you get it into 3d normal form. WHAT TO TURN IN: A UML model of what this data looks like in first normal form.

A UML model of what this data looks like in 2nd normal form.

A UML model of what this data looks like in 3rd normal form.

The relation scheme of your 3rd normal form model.