Consider a MOVIE database in which data is recorded about the movie industry. The data requirements are summarized as follows:

— Each movie is identified by title and year of release. Each movie has a length in minutes and a rating (e.g., G, PG, or R). Each has a production company, and each is classified under one or more genres(such as horror, action, drama, and so forth). Each movie has one or more directors and one or more actors appearing in it. Each movie also has a plot outline. Finally, each movie has zero or more quotable quotes, each of which is spoken by a particular actor appearing in the movie. An actor may have multiple quotes from one movie. 

— Actors are identified by name and date of birth and appear in one or more movies. Each actor has a role in the movie, i.e., the character he or she plays. 

— Directors are also identified by name and date of birth and direct one or more movies. 

— Production companies are identified by name and each has an address. A production company produces one or more movies.

Construct the E-R diagram for the Movie database. Document any assumptions that you make, but do not change the given requirements. Make sure that your model properly depicts entities, attributes and relationships, and weak entities (if any).

Note: It is very important that you stay within the bounds of the problem as stated above.  Take the description above referring to actors as an example, it does not mention or imply anything about if an actor is associated with any production company. Therefore, don’t even attempt to create a model that connects them.  

It is also very important that you do not add attributes if they are not necessary or required by the problem. Take the description above referring to actors as an example, it does not mention or imply anything about actor gender. Therefore, don’t even attempt to create a model that includes gender as an attribute, it would be superfluous.

These are just examples of how you can find yourself out of bounds. Developing a general, all-inclusive model to solve the problem is as important as creating a well-bounded model to solve the problem.