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[answered] MET CS 669 Database Design and Implementation for Business


Please assist with the following , Need this done in oracle.Thank you!


MET CS 669 Database Design and Implementation for Business

 

SQL Lab 5 Instructions: Subqueries, Expressions, and Value Manipulation Objective This lab teaches you the power of subqueries, expressions, and value manipulation, and the mechanics crafting SQL queries that harness the power of these three to handle more complex use cases. Prerequisites Before attempting this lab, it is best to read the textbook and lecture material covering the objectives listed above. While this lab shows you how to create and use these constructs in SQL, the lab does not explain in full the theory behind the constructs, as does the lecture and textbook. Required Software The examples in this lab will execute in modern versions of Oracle and Microsoft SQL Server as is. If you are using a different RDBMS, you may need to modify the SQL for successful execution. Saving Your Data If you choose to perform portions of the assignment in different sittings, it is important to commit your data at the end of each session. This way, you will be sure to make permanent any data changes you have made in your curent session, so that you can resume working without issue in your next session. To do so, simply issue this command: COMMIT; Data changes in one session will only be visible only in that session, unless they are committed, at which time the changes are made permanent in the database. Lab Completion Use the submission template provided in the assignment inbox to complete this lab. Page 1 of 51 Copyright 2016 Boston University. All Rights Reserved. Lab Overview In this lab, we practice value manipulation and subqueries on the schema illustrated below. 1..1

 

has Store_location uses 0..* store_location_id {pk}

 

store_name

 

currency_accepted_id {fk1} 1..1

 

has

 

1..* 0..* Offers 1..1

 

Currency Sells offers_id {pk}

 

store_location_id {fk1}

 

purchase_delivery_offering_id {fk2} currency_id {pk}

 

currency_name

 

us_dollars_to_currency_ratio sells_id {pk}

 

product_id {fk1}

 

store_location_id {fk2} 0..* 0..*

 

has has

 

1..1 1..1

 

Purchase_delivery_offering

 

purchase_delivery_offering_id {pk}

 

offering Product Sizes product_id {pk}

 

product_name

 

price_in_us_dollars sizes_id {pk}

 

size_option 1..1 1..1

 

has

 

1..*

 

Available_in 0..* has available_in_id {pk}

 

product_id {fk1}

 

sizes_id {fk2} This schema?s structure supports basic product and currency information for an international organization, including store locations, the products they sell and their sizes, purchase and delivery offerings, the currency each location accepts, as well as conversion factors for converting from U.S. dollars into the accepted currency. This schema models prices and exchange rates at a specific point in time. While a real?world schema would make provision for changes to prices and exchange rates over time, the tables needed to support this have been intentionally excluded from our schema, because their addition would add unneeded complexity on your journey of learning Page 2 of 51 subqueries, expressions, and value manipulation. The schema has just the right amount of complexity for your learning. The data for the tables is listed below. Currencies Name Ratio British Pound 0.66 Canadian Dollar 1.33 US Dollar 1.00 Euro 0.93 Mexican Peso 16.75 Store Locations Name Currency Berlin Extension Euro Cancun Extension Mexican Peso London Extension British Pound New York Extension US Dollar Toronto Extension Canadian Dollar Product Name US Dollar Price Casmir Sweater $100 Designer Jeans $150 Flowing Skirt $125 Silk Blouse $200 Wool Overcoat $250 Sells Store Location Product Berlin Extension Casmir Sweater Berlin Extension Designer Jeans Berlin Extension Silk Blouse Berlin Extension Wool Overcoat Cancun Extension Designer Jeans Cancun Extension Flowing Skirt Cancun Extension Silk Blouse London Extension Casmir Sweater London Extension Designer Jeans London Extension Flowing Skirt London Extension Silk Blouse Page 3 of 51 London Extension New York Extension New York Extension New York Extension New York Extension New York Extension Toronto Extension Toronto Extension Toronto Extension Toronto Extension Toronto Extension Wool Overcoat Casmir Sweater Designer Jeans Flowing Skirt Silk Blouse Wool Overcoat Casmir Sweater Designer Jeans Flowing Skirt Silk Blouse Wool Overcoat Purchase_delivery_offering Offering Purchase In Store Purchase Online, Ship to Home Purchase Online, Pickup in Store Offers Store Location Berlin Extension Cancun Extension London Extension London Extension London Extension New York Extension New York Extension Toronto Extension Purchase Delivery Offering Purchase In Store Purchase In Store Purchase In Store Purchase Online, Ship to Home Purchase Online, Pickup in Store Purchase In Store Purchase Online, Pickup in Store Purchase In Store Sizes Size Option Small Medium Large Various 2 4 6 8 10 Page 4 of 51 12 14 16 Available_in Product Casmir Sweater Casmir Sweater Casmir Sweater Designer Jeans Flowing Skirt Flowing Skirt

 

Flowing Skirt

 

Flowing Skirt

 

Flowing Skirt

 

Flowing Skirt

 

Flowing Skirt

 

Flowing Skirt

 

Silk Blouse

 

Silk Blouse

 

Silk Blouse

 

Wool Overcoat

 

Wool Overcoat

 

Wool Overcoat DDL and DML to create and populate the tables in the schema is listed below. Size Option Small Medium Large Various 2 4 6 8 10 12 14 16 Small Medium Large Small Medium Large DROP TABLE Sells; DROP TABLE Offers; DROP TABLE Available_in; DROP TABLE Store_location; DROP TABLE Product; DROP TABLE Currency; DROP TABLE Purchase_delivery_offering; DROP TABLE Sizes; CREATE TABLE Currency ( currency_id DECIMAL(12) NOT NULL PRIMARY KEY, currency_name VARCHAR(255) NOT NULL, us_dollars_to_currency_ratio DECIMAL(12,2) NOT NULL); CREATE TABLE Store_location ( store_location_id DECIMAL(12) NOT NULL PRIMARY KEY, store_name VARCHAR(255) NOT NULL, currency_accepted_id DECIMAL(12) NOT NULL); CREATE TABLE Product ( product_id DECIMAL(12) NOT NULL PRIMARY KEY, Page 5 of 51 product_name VARCHAR(255) NOT NULL, price_in_us_dollars DECIMAL(12,2) NOT NULL); CREATE TABLE Sells ( sells_id DECIMAL(12) NOT NULL PRIMARY KEY, product_id DECIMAL(12) NOT NULL, store_location_id DECIMAL(12) NOT NULL); CREATE TABLE Purchase_delivery_offering ( purchase_delivery_offering_id DECIMAL(12) NOT NULL PRIMARY KEY, offering VARCHAR(255) NOT NULL); CREATE TABLE Offers ( offers_id DECIMAL(12) NOT NULL PRIMARY KEY, store_location_id DECIMAL(12) NOT NULL, purchase_delivery_offering_id DECIMAL(12) NOT NULL); CREATE TABLE Sizes ( sizes_id DECIMAL(12) NOT NULL PRIMARY KEY, size_option VARCHAR(255) NOT NULL); CREATE TABLE Available_in ( available_in_id DECIMAL(12) NOT NULL PRIMARY KEY, product_id DECIMAL(12) NOT NULL, sizes_id DECIMAL(12) NOT NULL); ALTER TABLE Store_location ADD CONSTRAINT fk_location_to_currency FOREIGN KEY(currency_accepted_id) REFERENCES Currency(currency_id); ALTER TABLE Sells ADD CONSTRAINT fk_sells_to_product FOREIGN KEY(product_id) REFERENCES Product(product_id); ALTER TABLE Sells ADD CONSTRAINT fk_sells_to_location FOREIGN KEY(store_location_id) REFERENCES Store_location(store_location_id); ALTER TABLE Offers ADD CONSTRAINT fk_offers_to_location FOREIGN KEY(store_location_id) REFERENCES Store_location(store_location_id); ALTER TABLE Offers ADD CONSTRAINT fk_offers_to_offering FOREIGN KEY(purchase_delivery_offering_id) REFERENCES Purchase_delivery_offering(purchase_delivery_offering_id); ALTER TABLE Available_in ADD CONSTRAINT fk_available_to_product FOREIGN KEY(product_id) REFERENCES Product(product_id); ALTER TABLE Available_in ADD CONSTRAINT fk_available_to_sizes FOREIGN KEY(sizes_id) REFERENCES Sizes(sizes_id); INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio) VALUES(1, 'Britsh Pound', 0.66); INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio) VALUES(2, 'Canadian Dollar', 1.33); Page 6 of 51 INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio) VALUES(3, 'US Dollar', 1.00); INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio) VALUES(4, 'Euro', 0.93); INSERT INTO Currency(currency_id, currency_name, us_dollars_to_currency_ratio) VALUES(5, 'Mexican Peso', 16.75); INSERT INTO Purchase_delivery_offering(purchase_delivery_offering_id, offering) VALUES (50, 'Purchase In Store'); INSERT INTO Purchase_delivery_offering(purchase_delivery_offering_id, offering) VALUES (51, 'Purchase Online, Ship to Home'); INSERT INTO Purchase_delivery_offering(purchase_delivery_offering_id, offering) VALUES (52, 'Purchase Online, Pickup in Store'); INSERT INTO Sizes(sizes_id, size_option) VALUES(1, 'Small'); INSERT INTO Sizes(sizes_id, size_option) VALUES(2, 'Medium'); INSERT INTO Sizes(sizes_id, size_option) VALUES(3, 'Large'); INSERT INTO Sizes(sizes_id, size_option) VALUES(4, 'Various'); INSERT INTO Sizes(sizes_id, size_option) VALUES(5, '2'); INSERT INTO Sizes(sizes_id, size_option) VALUES(6, '4'); INSERT INTO Sizes(sizes_id, size_option) VALUES(7, '6'); INSERT INTO Sizes(sizes_id, size_option) VALUES(8, '8'); INSERT INTO Sizes(sizes_id, size_option) VALUES(9, '10'); INSERT INTO Sizes(sizes_id, size_option) VALUES(10, '12'); INSERT INTO Sizes(sizes_id, size_option) VALUES(11, '14'); INSERT INTO Sizes(sizes_id, size_option) VALUES(12, '16'); ??Casmir Sweater INSERT INTO Product(product_id, product_name, price_in_us_dollars) VALUES(100, 'Casmir Sweater', 100); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10000, 100, 1); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10001, 100, 2); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10002, 100, 3); ??Designer Jeans INSERT INTO Product(product_id, product_name, price_in_us_dollars) VALUES(101, 'Designer Jeans', 150); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10003, 101, 4); ??Flowing Skirt INSERT INTO Product(product_id, product_name, price_in_us_dollars) VALUES(102, 'Flowing Skirt', 125); Page 7 of 51 INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10004, 102, 5); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10005, 102, 6); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10006, 102, 7); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10007, 102, 8); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10008, 102, 9); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10009, 102, 10); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10010, 102, 11); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10011, 102, 12); ??Silk Blouse INSERT INTO Product(product_id, product_name, price_in_us_dollars) VALUES(103, 'Silk Blouse', 200); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10012, 103, 1); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10013, 103, 2); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10014, 103, 3); ??Wool Overcoat INSERT INTO Product(product_id, product_name, price_in_us_dollars) VALUES(104, 'Wool Overcoat', 250); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10015, 104, 1); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10016, 104, 2); INSERT INTO Available_in(available_in_id, product_id, sizes_id) VALUES(10017, 104, 3); ??Berlin Extension INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id) VALUES(10, 'Berlin Extension', 4); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1000, 10, 100); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1001, 10, 101); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1002, 10, 103); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1003, 10, 104); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(150, 10, 50); ??Cancun Extension INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id) VALUES(11, 'Cancun Extension', 5); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1004, 11, 101); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1005, 11, 102); Page 8 of 51 INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1006, 11, 103); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(151, 11, 50); ??London Extension INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id) VALUES(12, 'London Extension', 1); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1007, 12, 100); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1008, 12, 101); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1009, 12, 102); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1010, 12, 103); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1011, 12, 104); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(152, 12, 50); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(153, 12, 51); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(154, 12, 52); ??New York Extension INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id) VALUES(13, 'New York Extension', 3); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1012, 13, 100); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1013, 13, 101); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1014, 13, 102); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1015, 13, 103); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1016, 13, 104); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(155, 13, 50); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(156, 13, 52); ??Toronto Extension INSERT INTO Store_location(store_location_id, store_name, currency_accepted_id) VALUES(14, 'Toronto Extension', 2); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1017, 14, 100); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1018, 14, 101); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1019, 14, 102); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1020, 14, 103); INSERT INTO Sells(sells_id, store_location_id, product_id) VALUES(1021, 14, 104); INSERT INTO Offers(offers_id, store_location_id, purchase_delivery_offering_id) VALUES(157, 14, 50); Page 9 of 51 Section One ? Expressions and Value Manipulation Overview While it is certainly useful to directly extract values as they are stored in a database, it is more useful in some contexts to manipulate these values to derive a different result. In this section we practice using value manipulation techniques to transform data values in useful ways. For example, what if we want to tell a customer exactly how much money they need to give for a purchase? We could extract a price and sales tax from the database, but it would be more useful to compute a price with tax as a single value by multiplying the two together and rounding appropriately, and formatting it as a currency, as illustrated in the figure below. Less Useful to Customer More Useful to Customer price tax_percent price_with_tax 7.99 8.5 $8.67 We do not need to store the price with tax, because we can derive it when we need it. As another example, what if we need to send an email communication to a customer by name? We could extract the prefix, first name, and last name of the customer, but it would be more useful to properly format the name by combining them in proper order, as illustrated below. Less Useful to Customer More Useful to Customer prefix first_name last_name name Mr. Seth Nemes Mr. Seth Nemes Again, we do not need to store the formatted name, because we can derive it when we need it from its constituent parts. Manipulating raw data values stored in database tables can yield a variety of useful results we need without adding the burden of storing every such result. Steps 1. Execute the DDL and DML listed in the lab overview section in order to create and populate the currency schema in your database. There is no need to provide screenshots of executing this DDL and DML. 2. If we were asked to give the price of the Casmir sweater in Euros, how would we do it using SQL? In looking in the Product table, we see that we easily pull out the price of Page 10 of 51 the sweater in U.S. Dollars with a simple query: SELECT price_in_us_dollars FROM Product WHERE product_name = 'Casmir Sweater' The results of this query tell us that the price is $100: This is a good first step, but still does not give us the price in Euros, only in U.S. Dollars. Taking a look at the Currency table, we see that there is a column named us_dollars_to_currency_ratio that has the conversion factor needed convert from U.S. Dollars into the target currency. We can again write a simply query to list out the conversion factor for Euros: SELECT us_dollars_to_currency_ratio FROM Currency WHERE currency_name = 'Euro' This tells us that the conversion factor is 0.93: We then manually hardcode 0.93 into our first query in order to obtain the price in Euros as follows: SELECT price_in_us_dollars * 0.93 AS price_in_euros FROM Product WHERE product_name = 'Casmir Sweater' In the query, we have taken the price in U.S. Dollars and multiplied it times the conversion factor of 0.93, then aliased the result as price_in_euros, which gives us the result of ?93, as shown in the side?by?side screenshots of Oracle then SQL Server below. Page 11 of 51 You could have quickly answered the question of the price of a Casmir sweater in Euros by eyeballing the values in the tables and doing some basic math, so why did we use SQL? Perhaps the most significant reason is that when we are developing a database and surrounding I.T. system, we are not actually asking for a single answer, but are asking for logic that is capable of answering that same question repeatedly for the entire life of the database, whether it is years or decades. Answering the question just once is not useful given our goal. We want to know the price of the Casmir sweater today, tomorrow, and next year, even if the prices or exchange rates change. A second reason is that our goal is to give an I.T. system the ability to answer this question, not a human being. I.T. systems and surrounding databases are all about automation, performing repeated tasks much more quickly than human beings, freeing us from the responsibility of doing tedious tasks ourselves. Obviously, I.T. systems are not capable of eyeballing, and need formal SQL logic in order to access relational databases. Lastly, many real?world relational database schemas contain too many tables, relationships, and values for us to practically keep track them ourselves, so even if we want to answer a question ourselves, we still need to use SQL to obtain the values we need from the database. We develop SQL queries to answer our data questions in today?s world. 3. What if you are asked to give the price of a flowing skirt in Cancun? Just like in step 2, you need two queries. Your first query retrieves the currency ratio for the currency accepted in Cancun. Your second query hardcodes the currency ratio retrieved in the first query, in order to determine the price in Cancun. Note that unlike the use case in step 2, which asks for a currency by name, this use case asks for the store location by name in order to retrieve whatever currency is accepted by that location. This requires slightly different logic, so your first query will be similar to the first query in step 2, but will need to include the store location in order to retrieve the correct currency ratio. Capture a screenshots of both queries and the results of their execution. 4. Later we?ll explore directly embedding the second query inside the first. After all, you may have already insightfully objected to hardcoding the value of 0.93 in step 2, noticing that the resulting query will yield incorrect results as soon as the ratio changes, and likewise for step 3. You may be eager to fix this problem, but let?s first talk about money. How can we learn about subqueries when we are talking about values such as 93.0000 Euros as indicated by SQL Server? This does not look right! Each step is important in the learning process, and let us not rush this complex subject of subqueries. Page 12 of 51 SQL clients are sophisticated applications, but are not so sophisticated that they always display values in the format we expect. You may notice that the result from step 2 lists out no decimal points for Oracle and 4 decimal points for SQL Server, both without the Euro symbol (?). This is not what we are accustomed to seeing for monetary amounts in Euros. We would expect to see the monetary result as ?93.00 in the United States or United Kingdom, or ?93,00 in other European countries. Listing out 4 decimal points is nothing more than the default for the SQL Server Management Studio (SSMS) client when displaying numbers that are not whole numbers; SSMS does not understand that we are displaying a currency. The default for the Oracle SQL Developer client is to remove trailing 0 digits that occur to the right of the decimal point, hence the result of 93 with no decimal points. Other SQL clients may have different defaults for SQL Server and other databases. SQL clients oftentimes display a value from a basic SQL query in a nonconventional format. The discrepancy between the value displayed and the value we conventionally expect shows us that there is something more involved. There are actually four significant components that determine how a value is displayed ?? the raw value stored in a database table, manipulations on the value performed by the SQL query, formatting constructs applied in the SQL query, and how the particular SQL client displays the value. These components all collectively determine how a value will be displayed to us when we execute SQL in a SQL client. There is a tremendous amount of depth for each of these components, and while we will not be able to cover every detail, it is important that we explore each in more depth. Doing so will help give you the ability to craft queries that display values in whatever format you deem appropriate. Controlling how a value is displayed is an intricate subject. Different kinds of data have different limits that present a challenge for database designers as they consider how to store the data. Some kinds of values have no theoretical limit, for example, fractions that result in infinitely repeating decimals. How do we store these infinitely long values? Some kinds of values have theoretical limits, but we cannot determine them. For example, how would we determine how much storage we need for the text of the lengthiest book in the world? Even if we determine the lengthiest book known, we could always discover a new, lengthier book, or someone could write a lengthier one in the future. Some kinds of values have known limits, but their limits are too big for practical storage. For example, a business may know all websites visited by its employees while at work in the prior year, but would it practical for the business to store the full content of every website every time it is visited? We need to think about these kinds of limits before we store the data in our database. All values stored in a relational database column have size limits, and interestingly datatypes, which we learned in prior labs determine the set of legal values for database columns, also establish size limits. All exact numeric datatypes have a precision, which is the maximum number of digits allowed in the number, and a Page 13 of 51 scale, which is the maximum number of digits allowed to the right of the decimal point. For example, if we want to store the number 12.34, we need a precision of at least 4 since there are 4 digits in total, and a scale of at least 2 since there are 2 digits to the right of the decimal point. All inexact numeric datatypes used for storing fractional numbers are constrained by a maximum number of bytes. All text datatypes have a maximum limit of either characters or bytes. Date and time datatypes have limits on the number of digits used to store fractions of a second. Data stored in a relational database is limited in size in various ways as specified in the datatype for each table column. 5. The legal form and size limitation of a value is one significant component that determines how a value will be displayed in the result of a SQL query, and you get a chance to explore it for yourself in this step. We now know it is the datatype that constrains to a set of legal values and a size limitation. Identify any one of the datatypes refer...

 


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