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Connecting with Computer Science, 2e

Connecting with Computer Science, 2e. Chapter 6 Database Fundamentals. Objectives. In this chapter you will: Consider the widespread use of databases Take a brief tour of database development history Learn basic database concepts Be introduced to popular database management software

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Connecting with Computer Science, 2e

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  1. Connecting with Computer Science, 2e Chapter 6 Database Fundamentals

  2. Objectives • In this chapter you will: • Consider the widespread use of databases • Take a brief tour of database development history • Learn basic database concepts • Be introduced to popular database management software • See how normalization makes your data more organized Connecting with Computer Science, 2e

  3. Objectives (cont’d.) • In this chapter you will (cont’d.): • Explore the database design process • Understand data relationships • Gain an understanding of Structured Query Language ( SQL ) • Learn some common SQL commands Connecting with Computer Science, 2e

  4. Why You Need to Know About...Databases • Data must be organized • Effective computer professionals know correct database design • Normalization • Ensures an accurate and reliable database • Structured Query Language ( SQL ) • Describes how information is retrieved from relational database Connecting with Computer Science, 2e

  5. Database Applications • Database • Data logically related and organized into a file or set of files to allow access and use • Database applications • Student grading and library inventory • Genealogy studies and Social Security payments • Real estate sales, video store rentals, and retail sales • Space shuttle missions • Database development • Essential part of computer professional’s daily life Connecting with Computer Science, 2e

  6. Brief History of Database Management Systems • 1970 to 1975 • Work of IBM employees E. F. Codd and C. J. Date • Created theoretical model for designing data structures • Model became foundation for database design • Software for organizing and sorting data • System R by IBM and Ingres by UC-Berkeley • Structured Query Language ( SQL ) • SQL: database standard • Database management system ( DBMS ) for PCs • 1978 • C. Wayne Ratliff of Martin Marietta develops Vulcan Connecting with Computer Science, 2e

  7. Brief History of Database Management Systems (cont’d.) • 1980 to the present • Vulcan renamed dBASE II ( no dBase I ) • Popularity of dBASE II inspires other companies • Paradox, Microsoft Access, and FoxPro • Databases become essential for business • Corporate decision making • Systems: inventory management to customer support Connecting with Computer Science, 2e

  8. Brief History of Database Management Systems (cont’d.) Table 6-1, Popular database management systems Connecting with Computer Science, 2e

  9. Database Management System Fundamentals • Six main DBMS functions • Manage database security • Manage multiple users’ access to the database • Manage database backup and recovery • Ensure data integrity • Provide an end-user interface to the database • Provide a query language allowing users to modify and view database information easily Connecting with Computer Science, 2e

  10. Database Concepts • Basic database elements • Database: collection of one or more tables ( entities ) • Table or entity: divided into rows and columns • Row ( record or tuple ): collection of columns • Column ( field or attribute ): represents specific information • Domain: set of possible column values Connecting with Computer Science, 2e

  11. Database Concepts (cont’d.) Figure 6-1, A database table consists of rows and columns Connecting with Computer Science, 2e

  12. Indexes • Special files occupying their own space • Specify columns determining how information stored in a table can be accessed more efficiently • Examples: music database and the telephone book • Advantages • Flexibility: many different columns to sort against • Searching and retrieval speeds up • Disadvantages • Extra storage space • Updating takes longer Connecting with Computer Science, 2e

  13. An Example of Indexing Figure 6-2, You use database concepts in your everyday life Connecting with Computer Science, 2e

  14. An Example of Indexing (cont’d.) • Each database row has similar attributes • Sort key: one or more columns used to determine the data’s sort order • One key or a combination of keys determines sort order • Database information is stored in natural or sequential order • Order of records displayed equals the order records are entered Connecting with Computer Science, 2e

  15. An Example of Indexing (cont’d.) Figure 6-3, Database records sorted by using the UPC column as a key Connecting with Computer Science, 2e

  16. An Example of Indexing (cont’d.) Figure 6-4, Database records sorted by Brand_Name and Description Connecting with Computer Science, 2e

  17. Normalization • Set of rules dictating database design • Eliminates duplication and inconsistencies • Process: sequence of stages called normal forms • Five normal forms • Third normal form provides sufficient structure • Three database design problems solved • Representation of certain real-world items • Redundancies (repetitions) in data • Excluded and inconsistent information Connecting with Computer Science, 2e

  18. Preparing for Normalization: Gathering Columns • Make a list of all pertinent fields ( columns or attributes ) • Source of fields: end-user reports or song inventory • Write fields on the column list • Review user-specified input forms • Convert each field from the report to column in table Connecting with Computer Science, 2e

  19. Preparing for Normalization: Gathering Columns (cont’d.) Figure 6-5, End-user report with table columns highlighted Connecting with Computer Science, 2e

  20. Preparing for Normalization: Gathering Columns (cont’d.) Figure 6-6, Additional table columns can be gleaned from input forms Connecting with Computer Science, 2e

  21. Preparing for Normalization: Gathering Columns (cont’d.) • Reconcile fields in report to column list • Create tables of columns by combining associated fields • Logically group related information • Example: information on artist and song files • Gather data to create physical music database Connecting with Computer Science, 2e

  22. First Normal Form • Unnormalized table • Row-column intersection with two or more values • First normal form (1NF) eliminates redundancies • Create new record for duplicated column • Fill in blanks so all columns in record have a value • Columns with duplications: Album_Num, Album_Name, Artist_Code, Artist_Name, Media_Type, and Genre_Code • Remaining redundancies are addressed later Connecting with Computer Science, 2e

  23. Second Normal Form • Next steps: • Assign a primary key to the table • Identify functional dependencies within the table • Primary key (PK) • Column or combination of columns (composite) uniquely identifying a row within a table • Examples: Student ID or Artist_Code Connecting with Computer Science, 2e

  24. Second Normal Form (cont’d.) • Determinant: column(s) used to determine value assigned to another column in the same row • Example: Artist_Code determinant for Artist_Name • Functional dependency: column’s value dependent on another column’s value • Each value of first column is matched to single value in second • Example: Artist_Name functionally dependent on Artist_Code • Composite key: primary key made up of more than one column Connecting with Computer Science, 2e

  25. Second Normal Form (cont’d.) • Second normal form (2NF) • First normal form and non-PK columns functionally dependent on PK • Creating 2NF • Determine columns not dependent on PK • Remove columns and place in new table • Default 2NF: table without composite PK • Primary 2NF benefit: save disk space Connecting with Computer Science, 2e

  26. Second Normal Form (cont’d.) Figure 6-10, 2NF: Remove any columns that aren’t dependent on the composite primary key and create a new table Connecting with Computer Science, 2e

  27. Third Normal Form • Third normal form (3NF) • Eliminate transitive dependencies • Column dependent on another column not part of PK • Example: Genre_Desc depends on Genre_Code • Each nonkey field should be a fact about the PK Connecting with Computer Science, 2e

  28. Third Normal Form (cont’d.) Figure 6-11, Songs table with the Genre_Desc column added Connecting with Computer Science, 2e

  29. Third Normal Form (cont’d.) • Creating 3NF • Remove transitive dependencies • Place removed columns in new table • Primary 3NF benefit: eliminates repetition and saves disk space Connecting with Computer Science, 2e

  30. Third Normal Form (cont’d.) Figure 6-12, Songs and Genre tables in 3NF Connecting with Computer Science, 2e

  31. Third Normal Form (cont’d.) Figure 6-13, Eliminating repetition saves storage space Connecting with Computer Science, 2e

  32. The Database Design Process • Six steps to designing a normalized database • Example: • Creation of student-grading system Connecting with Computer Science, 2e

  33. Step 1: Investigate and Define • Investigate and research modeled information • Define purposes and uses of the database • Use any documents end users work with to complete tasks • Involve end users in design process • Example: student-grading system based on a course syllabus Connecting with Computer Science, 2e

  34. Step 2: Make a Master Column List • Create list of fields for information • Possible field properties: • Field name • Data type (char, varchar, number, date, etc.) • Length • Number of decimal places (if any) • Review users’ documents for fields • Forms and reports good source for fields • Example fields: Student ID, First Name, Last Name, E-Mail, Grade Level, Grade Level Description, Homework Average, Quiz Average, Test Average Connecting with Computer Science, 2e

  35. Step 3: Create the Tables • Logically group defined columns into tables • Heart of design process • Relies heavily on normalization rules • Main rules in database design: 1NF – 3NF • Table in 3NF: well defined • Normalizing databases • Like cleaning a closet Connecting with Computer Science, 2e

  36. Step 3: Create the Tables (cont’d.) Figure 6-14, Tables created for the student-grading system Connecting with Computer Science, 2e

  37. Step 4: Work on Relationships • Relationship: defines table relations • Two types of relationships discussed in this chapter • One-to-many (1:M) • One-to-one (1:1) • Primary and foreign keys are defined in each of the tables • Primary key (PK): determinant discussed earlier • Foreign key (FK): column in one table is PK in another Connecting with Computer Science, 2e

  38. Step 4: Work on Relationships (cont’d.) • One-to-many (1:M) • Most common relationship • Each record in Table A relates to multiple records in Table B • Requires that FK column(s) in “many” table refer to PK column in “one” table • Example: • Grades Table to Student Table Connecting with Computer Science, 2e

  39. Step 4: Work on Relationships (cont’d.) Figure 6-15, The relationship of Student to Course Grade is one-to-many (1:M) Connecting with Computer Science, 2e

  40. Step 4: Work on Relationships (cont’d.) • One-to-one (1:1) • For every record in Table A, there can be one and only one matching record in Table B • Consider combining tables in 1:1 relationship • Normally the two tables can be combined into one table Connecting with Computer Science, 2e

  41. Step 4: Work on Relationships (cont’d.) Figure 6-16, The relationship of Student to Grade Level is one-to-one (1:1) Connecting with Computer Science, 2e

  42. Step 5: Analyze the Design • Analyze work completed • Search for design errors and refine tables as needed • Follow normalization forms (ideally to 3NF) • Correct any violations • ER models • Visual diagram composed of entities and relationships • Entities represent the database tables • Relationships show how tables relate to each other • Cardinality • Shows numeric relations between entities Connecting with Computer Science, 2e

  43. Step 5: Analyze the Design (cont’d.) • Types of cardinality ( and their notation ) include: • 0..1, 0:1 ( zero to one ) • 0..M, 0:N, 0..*, 0..n ( zero to many ) • 1..1, 1:1 ( one to one ) • 1..M, 1:M, 1:N, 1..*, 1..n ( one to many ) • M..1, M:1, N:1, *..1, n..1 ( many to one ) • M..M, M:M, N:N, *..*, n..n ( many to many ) • Example: • ER model for the student-grading system Connecting with Computer Science, 2e

  44. Step 5: Analyze the Design (cont’d.) Figure 6-17, The student-grading system ER model in Visio Connecting with Computer Science, 2e

  45. Step 6: Reevaluate • Reevaluate database performance • Ensure database meets all reporting and form needs • Include the end users • Explain each table and field being used • Make sure fields are defined to users’ requirements • Manipulate data structure with SQL commands Connecting with Computer Science, 2e

  46. Structured Query Language (SQL) • Structured Query Language (SQL) functions • Manipulate data • Define data • Administer data • Many different “dialects” of SQL Connecting with Computer Science, 2e

  47. Structured Query Language (SQL) (cont’d.) • SQL advantages • Reduces training time (syntax based in English) • Makes applications portable (SQL is standardized) • Reduces the amount of data being transferred • Increases application speed • Following sections show basic SQL commands • Creating tables • Adding (inserting) rows of data • Querying table to select certain information Connecting with Computer Science, 2e

  48. Structured Query Language (SQL) (cont’d.) Figure 6-18, A sample SQL statement and results Connecting with Computer Science, 2e

  49. CREATE TABLE Statement • CREATE TABLE statement: make a new table • NULL/NOT NULL • Optional property indicates whether data required • Syntax: Connecting with Computer Science, 2e

  50. CREATE TABLE Statement (cont’d.) • SQL statements to create table called Songs: Connecting with Computer Science, 2e

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