Question: Question 2 ( 3 points - 1 point per query ) : consider a Digital Games database with the following relational schemas: Gamer: ( UserID
Question points point per query: consider a Digital Games database with the following relational schemas:
Gamer: UserID Name, Age, Address, City, FavoriteGame, Rank
Game: GameID Name, Company, ReleaseYear, Version
Store: StoreID Name, Address, City
WhereToFind: GameID StoreID, Price
Category: GameID CategoryName
The database has information about registered gamers as well as their favorite games and their ranks. For simplicity,
let us assume that the database stores only one favorite game per gamer. This database also has information about
famous digital games, the stores that sell them, and the price of each game. Each store sells many games, and copies
of a single game can be sold in different stores. The whereToFind relation captures such a manytomany
relationship. GameID and ShopID in WhereToFind relation are foreign keys referencing the primary keys of the
Game and Shop relations, respectively. FavoriteGame in the Gamers relation is a foreign key referencing the
primary keys of the Game relation. Each game can fall within several categories eg sports, action stored in the
Category relation. GameID in Category relation is a foreign key referencing the primary key of the Game.
From the relational schemas illustrated above, use the different relational algebra operators and operations rho sigma pi
discussed in class to build the following queries:
For each possible game, list the name and category of the game as well as the name and address of the store
selling it Name the result as FullInfo
Display stores by name that sell a game named NBA for less than $ included NBA is the name of
the game, not the games ID Name the result as WhereToFindNBA
Since each game can fall within several categories, list games by name and release year that are in the
Action category and also in the RolePlaying category.
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