Tuesday, September 28, MIS Lecture Notes1 Midterm Exam: Tuesday, September 28 Covers all course material through today’s lecture: –Homeworks 1 - 5; Problem 1 of Homework #6 –All lectures –All handouts –Readings from Stair and Reynolds and Rob and Coronel Will be pencil-and-paper –No computers or calculators are necessary Will include: –Relational database design (about 60%) –Short answer/True-False (about 30%/10%) Review sessions will be held: –Wednesday 9/29, 5:30 PM - 7 PM, Room 255 Baker Hall –Saturday 10/2, 1 PM - 4 PM, Room 1003 HbH Homework #6 (Queries) due Tuesday, October 12

Tuesday, September 28, MIS Lecture Notes2 Query Logic and Design Queries are sequences of commands used to extract and combine data. Using the department store example, what are typical questions that could be answered via queries? – While tables store data, and relationships encode business rules that link data in multiple tables, only queries assist in the transformation of data into information

Tuesday, September 28, MIS Lecture Notes3 Design and Use of Queries In Access, queries are designed and executed using three views: –Design view –Datasheet view –SQL view Queries are used: –to perform ad-hoc data analysis –to populate recordsets used in forms –to populate controls such as combo boxes and text boxes –to organize data for reports –as part of macros and code for custom processing tasks

Tuesday, September 28, MIS Lecture Notes4 Structured Query Language Queries are executed using Structured Query Language (SQL). SQL has a number of advantages compared to sequential programs implemented in third-generation languages such as C or Pascal: –SQL is simple: there are only thirty or so commands –SQL is nonprocedural: data storage details are shielded from the user; –SQL is platform-independent: queries can be represented identically on different platforms; –SQL is GUI-compatible: queries can be written in Access and other languages with a GUI alone. You may eventually find that some queries are more easily expressed using SQL than via an interface

Tuesday, September 28, MIS Lecture Notes5 SQL SELECT Many queries are based on the command which extracts a set of records from a table for further analysis: SELECT  Design View SQL View   Datasheet View

Tuesday, September 28, MIS Lecture Notes6 SQL SELECT (cont’d) The general SELECT command uses many clauses : SELECT [INTO ] FROM [WHERE ] [GROUP BY ] The SELECT command can extract a set of columns from one table, or a set of columns from different tables for which pairs of tables have Foreign Key relationships. The INTO clause specifies a table which will store results of this query. The FROM clause lists the table(s) used in the query The WHERE clause lists logical conditions that must be satisfied. The GROUP BY clause lists the attributes by which rows will be aggregated.

Tuesday, September 28, MIS Lecture Notes7 SQL SELECT- Using String Expressions New columns in queries may be assigned values using a variety of string expressions: –[ColName1] & [ColName2] (concatenation): appends one string field to another. Ex: [SName] & “ - “ & [Sex] applied to “Richards” & “ - “ “M” yields “Richards - M” –Left([ColName], N): extracts first N characters of column ColName. Similarly: Right([ColName], N) Ex: Left([SName], 1) applied to “Richards” yields “R” –Mid([ColName], N, M): extracts M characters of column ColName starting at character #N Ex: Mid([CrsNbr],4,1) applied to “ACC610” yields “6” –Len([ColName]): returns the length of column ColName Ex: Len([SID]) applied to “218” yields 3.

Tuesday, September 28, MIS Lecture Notes8 SQL SELECT- Using Arithmetic Expressions New columns in queries may also be assigned values using a variety of arithmetic expressions: –[ColName1] + [ColName2] (addition): add the contents of two columns. Similarly for “-” (subtraction) –[ColName1] * [ColName2] (multiplication): subtracts the contents of one column from another. Similarly for “/” (division) –[ColName]^N (exponentiation): computes the value of a column to the power N (N can be any real number) –Abs([ColName]) (absolute value): computes the absolute value of a column ex.: Abs([Temperature]) applied to “-32” gives “32” –Int([ColName]): returns the integer portion of a real number ex.: Int([InterestRate]) applied to “8.325” gives “8”

Tuesday, September 28, MIS Lecture Notes9 SQL WHERE: Criteria for Selecting Rows Logical expressions can determine rows to appear in query output: –WHERE LogicalExpression([ColName]) Ex: WHERE Left([Sname],1) < “S” chooses all rows where the first initial of the last name is lower than “S” Ex: WHERE Major Is Null selects all rows where the value of the field “Major” is Null (empty) Other operators: BETWEEN, LIKE, IN, EXISTS –WHERE LogicalExpression1([ColName1]) OR LogicalExpression2([ColName2]) (Logical OR) Ex: (Left([SName],1) 2.5) chooses all rows where the first initial of the last name is lower than “P” OR the GPA exceeds 2.5 –WHERE LogicalExpression1([ColName1]) AND LogicalExpression2([ColName2]) (Logical AND) Ex: (Left([SName],1) 2.5) chooses all rows where the first initial of the last name is lower than “P” AND the GPA exceeds 2.5

Tuesday, September 28, MIS Lecture Notes10 SQL GROUP BY: Aggregate Records The GROUP BY clause divides records into groups based on the value of a criterion. New fields may be created using summarized values of fields in each category. Example: –SELECT Major, Avg(GPA) AS AvgOfGPA FROM STUDENT GROUP BY STUDENT.Major Other summary calculations: Sum, Min, Max, Count, StDev, Var, First, Last

Tuesday, September 28, MIS Lecture Notes11 Access Variations on SELECT Queries UPDATE: Calculates a new value for an existing column using arithmetic/string expressions for rows satisfying certain criteria. For example: –UPDATE FACULTY SET FACULTY.FName = "Kennedy-Jenkins" WHERE (([FName]="Kennedy"))

Tuesday, September 28, MIS Lecture Notes12 Access Variations on SELECT Queries (cont’d) APPEND: Adds records from the current query to another table with identical format. For example: – INSERT INTO FACULTY4 SELECT FACULTY3.* FROM FACULTY3;

Tuesday, September 28, MIS Lecture Notes13 Access Variations on SELECT Queries (cont’d) MAKE TABLE: Creates a new table based on results from the current query. For example: –SELECT FACULTY3.FID, FACULTY3.FName INTO FACULTY_NEW FROM FACULTY3 GROUP BY FACULTY3.FID, FACULTY3.FName ORDER BY FACULTY3.FID;

Tuesday, September 28, MIS Lecture Notes14 Access Variations on SELECT Queries (cont’d) DELETE: deletes all records satisfying certain criteria. Example: – DELETE STUDENT2.*, STUDENT2.GPA FROM STUDENT2 WHERE (((STUDENT2.GPA)<=2.5));

Tuesday, September 28, MIS Lecture Notes15 Access Variations on SELECT Queries (cont’d) CROSSTAB: creates a two-dimensional table in which a value field is summarized according to row and column field(s). Example: –TRANSFORM Avg(STUDENT.GPA) AS AvgOfGPA SELECT STUDENT.Major FROM STUDENT GROUP BY STUDENT.Major PIVOT STUDENT.Sex;

Tuesday, September 28, MIS Lecture Notes16 Parameter Queries Parameter Queries: Select records that match a user-defined criterion based on a particular field. Example: –SELECT COURSE.* FROM COURSE WHERE (((COURSE.CrsNbr)=[Select a course number:]));

Tuesday, September 28, MIS Lecture Notes17 Other Query Applications Views: Select a row driver, and add one or more tables, each pair of which is linked by a Foreign key relationship. Select columns of interest for output. Find Duplicate Records: Find the records in a table that have identical values for all columns Find Unmatched Records: Find the records in one table without records in another table according to a common field Archive: Select records according to given criteria (SELECT query); create a new table based on query results (MAKE TABLE query), delete selected records from original table (DELETE query) Select Top X Records: Select the top X (X%) records according to given criteria

Tuesday, September 28, MIS Lecture Notes18 Data Analysis Applications of Queries Suppose you have a number of fields that could serve as indexes or foreign keys, but with inconsistent information: –“Degree attained” = “Bachelor’s”, “B.A.”, “A.B”, “MPM”, “M.S.” How could you derive a consistent set of values? Now suppose you have a dataset describing customers with a number of candidate keys: –Last name, Phone number, Street address How could you determine a primary key (if one exists)? Finally, you want to determine the distribution of family income by marital status and gender: –“Marital Status” = “Married”, “Separated”, “Divorced”, … How would you calculate this distribution?