The following database has 5 transactions. Let min_sup = 60% and min_conf = 80%. TID Items_bought T100 {m, o, n, k, e, y} T200 {d, o, n, k, e, y} T300 {m, a, k, e} T400 {m, u, c, k, y} T500 {c, o, k, i, e} (2) Select all the strong association rules in the format of {item 1, item 2} -> {item 3}. ☐ {k, o} -> {e} ☐ {k, e} -> {o} ☐ {k, e} -> {y} ☐ {e, o} -> {k} ☐ {k, y} -> {e} ☐ {e, y} -> {k}
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- Consider the following schema for a company database and answer the following questions: DEPARTMENT Dname Dnumber Mgr_ssn Mgr_start_date M EMPLOYEE Fname Minit Lname Ssn Bdate Address Sex Salary Dno WORKS ON Essn Pno Hours M PROJECT Pname Pnumber Plocation Q1//create all tables of this schema and apply any necessary constraints Q2// Write SQL statements that carry out the following tasks: 1-alter the employee table to add column city of 20 characters. 2- alter the employee table to apply the following business rule The minimum salary is 3000 and maximum salary is 10000 3- insert sample data in each table . 4- display a list of each employee SSN, Name and Salary. 5- display a list of each employee SSN , Name and Salary for employees working in department no 2.Consider the following schema for a company database and answer the following questions: DEPARTMENT Dname Dnumber Mgr_ssn Mgr start date M EMPLOYEE Fname Minit Lname Ssn Bdate Address Sex Salary Dno M WORKS ON Essn Pno Hours M PROJECT Pname Pnumber Plocation Q1//create all tables of this schema and apply any necessary constraints Q2// Write SQL statements that carry out the following tasks: 1-alter the employee table to add column city of 20 characters. 2- alter the employee table to apply the following business rule The minimum salary is 3000 and maximum salary is 10000 3- insert sample data in each table. 4- display a list of each employee SSN , Name and Salary. 5- display a list of each employee SSN, Name and Salary for employees working in department no 2.The following database has 5 transactions. Let min_sup = 60% and min_conf = 80%. TID Items_bought T100 {m, o, n, k, e, y} T200 {d, o, n, k, e, y} T300 {m, a, k, e} T400 {m, u, c, k, y} T500 {c, o, k, i, e}
- A database has 4 transactions, shown below.TID Date items_boughtT100 10/15/04 {K, A, D, B}T200 10/15/04 {D, A, C, E, B}T300 10/19/04 {C, A, B, E}T400 10/22/04 {B, A, D}Assuming a minimum level of support min_sup = 60% and a minimum level ofconfidencemin_conf = 80%:(a) Find all frequent itemsets (not just the ones with the maximum width/length) using the Apriori algorithm. Show your work—just showing the final answer is not acceptable. For each iteration show the candidate and acceptable frequent itemsets. You should show your work similar to the way the example was done in thePowerPoint slides.(b)List all of the strong association rules, along with their support and confidence values, whichmatch the following metarule, where X is a variable representing customers and itemi denotes variables representing items (e.g., “A”, “B”, etc.).x transaction, buys(X, item1) buys(X, item2) buys(X, item3)Hint: don’t worry about the fact that the statement above uses relations. The point of the…The following database will be used in this question: SALESREP SalesRepNo 654 734 345 434 RepName Jones Smith Chen Johnson HireDate 01/02/2005 02/03/2007 01/25/2004 11/23/2004 CUSTOMER CustNo 9870 8590 7840 4870 CustName Winston Gonzales Harris Miles Balance 500 350 SalesRepNo 345 434 654 345 800 100The following three tables make up a simple reservation system for a small campground. The database should allow for a camper to make multiple reservations for future dates and for a camping spot to have several reservations for upcoming visits. Table: Camper CAMPER_NUMBER 1000 1001 1002 1003 CAMPER_LAST_NAME Jones Schmidt Williams Cooper CAMPER_FIRST_NAME Jamie Pat Clifford Amanda CAMPER_ADDRESS 1278 Essex Pl 4954 Spangled Way 956 Segull Lane P. O. Box 998877 CAMPER_CITY Birmingham El Paso Portland Portsmouth CAMPER_STATE AL TX ME OH CAMPER_ZIP_CODE 45251 79919 4108 45662 CAMPER_DRIVERS_LICENSE JJ998743-98 87632434 WIL885123 765A876B897 CAMPER_EMAIL jjones@somewhere.com patwonderfu34l@nowhere.net williams98342@foomail.com coopera@nowhere.net Table: Spot SPOT_NUMBER 101 102 103 104 SPOT_NAME The Pines The Glade Teardrop Spot Tent In Trees…
- The following three tables make up a simple reservation system for a small campground. The database should allow for a camper to make multiple reservations for future dates and for a camping spot to have several reservations for upcoming visits. Table: Camper CAMPER_NUMBER 1000 1001 1002 1003 CAMPER_LAST_NAME Jones Schmidt Williams Cooper CAMPER_FIRST_NAME Jamie Pat Clifford Amanda CAMPER_ADDRESS 1278 Essex Pl 4954 Spangled Way 956 Segull Lane P. O. Box 998877 CAMPER_CITY Birmingham El Paso Portland Portsmouth CAMPER_STATE AL TX ME OH CAMPER_ZIP_CODE 45251 79919 4108 45662 CAMPER_DRIVERS_LICENSE JJ998743-98 87632434 WIL885123 765A876B897 CAMPER_EMAIL jjones@somewhere.com patwonderfu34l@nowhere.net williams98342@foomail.com coopera@nowhere.net Table: Spot SPOT_NUMBER 101 102 103 104 SPOT_NAME The Pines The Glade Teardrop Spot Tent In Trees SPOT_LENGTH 55 50 20 0 SPOT_PULLTHRU 1 0 0 0 SPOT_ELECTRIC_AMPS 50 50 20 0…An insurance company needs to store their salespeople’s information who are selling their insurance policies. They already have a database with multiple tables, one of the tables (Salesperson) stores information about each salesperson along with the bonus percent they receive, based on the city where the insurance is sold. The table has the following fields: Salesperson(spID, spName, spBirthDate,spCitySelling, bonusPercent) spID: Unique identification number of the salesperson. spName: Full name of the salesperson. spBirthDate: Birthdate of the salesperson. spCitySelling: The city in which the salesperson is selling the insurance. bonusPercent: The bonus percent received by the salesperson based on the city in which he/she sells the insurance. Each salesperson can sell the insurance in just one city. However, for a city, there can be more than one salesperson appointed. Also, the bonus percent is fixed for each city. For example, all of the salespeople who sells insurance in…Assume you have StudentsDB database stored in MYSQL server that contains the following 2 tables as follows: Table 1: Students Table 2: Grades int, Primary Key, Auto Increment Sid Gid int, Primary Key, Auto Increment UniversityID varchar(8), Unique Sid int, Foreign Key Name varchar(50) CourseCode varchar(10) Мajor varchar(50) Credits int CourseGrade varchar(4) *** Note: Students must use PDO to access MYSQL DB, MYSQLI isn't allowed *** Implement the following PHP scripts as required below: 1. displayStudentGrades.php: the script should display student's name and major. In addition, all courses codes with their grades should be displayed in HTML table followed by its Cumulative GPA, Total Credits Passed and Total Credits Registered at the bottom of the TABLE. Highlight the course row by red if it's F grade. The studentlD is received using HTTP GET method from an HTML form. 2. addnewStudentwithgrades.php: the script should obtain all the required student details using HTML form including…
- A relational database has the following schema: Customer (Cno, name, address, age, loan_type, loan_amount, year) Solve following queries in relational algebra: Select the records where loan_type is 'Personal'. Select the records where age is geater than 30 and loan_type is ‘soft’. Select records from Customer table where loan_type is ‘Personal' and loan_amount is Rs200000 or those customers who opened an account after 2017. Project the columns name and address from the Customer table.A database has four transactions. Let min sup = 60% and min conf = 80%.cust ID TID items bought (in the form of brand-item category)01 T100 {King’s-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread}02 T200 {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread}01 T300 {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie}03 T400 {Wonder-Bread, Sunset-Milk, Dairyland-Cheese}(a) At the granularity of item category (e.g., itemi could be “Milk”), for the ruletemplate,∀X ∈ transaction, buys(X,item1) ∧ buys(X,item2) ⇒ buys(X,item3) [s,c],list the frequent k-itemset for the largest k, and all the strong association rules(with their support s and confidence c) containing the frequent k-itemset for thelargest k.(b) At the granularity of brand-item category (e.g., itemi could be “Sunset-Milk”),for the rule template,∀X ∈ customer, buys(X,item1) ∧ buys(X,item2) ⇒ buys(X,item3),list the frequent k-itemset for the largest k (but do not print any rules).struct student { char name[20]; char studentID[10]; char phonenum [9]; char advisor [20]; float gpa; } Operations needed: addnewStudent { purpose: to create a new student in the database input: name, studentID.phonenum,advisor.gpa output: none } Name another operation besides add that we could use for our student data structure.