1. Chapter 9: Database Systems
9.1 Database Fundamentals
9.2 The Relational Model
9.3 Object-Oriented Databases
9.4 Maintaining Database Integrity
9.5 Traditional File Structures
9.6 Data Mining
9.7 Social Impact of Database Technology

2. Definition of a Database
A database (DB) is collection of data with internal links that allow users to access the data from a variety of perspectives.
Contrast: a flat file, which can only be accessed in one way.

3. Database
Database is multidimensional, since internal links between its entries make the information accessible from a variety of perspectives
Flat File is the traditional one-dimensional file storage system that presents its information from a single point of view

4. Example of a Flat File Panel 2.4.06 £3400 205 North Street Honeywell
Case
5.5.06
£2000
15 Retail Road
Univac 43
Plate
1.7.06
£1000
1 The Lane
Sperry 37
Product
Due date
Price
Customer address
Customer name
Order no.

5. Relational Database Order file Customer file Address Name Customer no.
Product
Due date
Price
Customer no.
Order no.
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
Order file
Customer file

6. Relational Database linkage Order file Customer file Address Name
Customer no.
Product
Due date
Price
Customer no.
Order no.
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
Order file
Customer file

7. Figure 9.1 A file versus a database organization

8. Databases emerged as a means of integrating the information stored and maintained by a particular organisation
Different departments/users can use the same data.
Benefits:
Reduce (or remove) duplication of data being stored in several places.
Ensure all departments use the same versions of the data
Easier to make changes to data -- change in one place only, not in several files

9. Advantages of a Single Central Database
Easier to control access to information.
Easier to update information.
Easier (overall) to maintain and upgrade as technology improves.

10. Potential problems of data integration
Security and Privacy issues
Sensitive data (personal data, salary records ...) may be accessed by unauthorised personnel
e.g. Need to restrict payroll user's access to exclude other financial data.
The ability to control access to information in the database is as important as the ability to share it

11. Schemas
To provide different users with different information within a database, database systems often rely on schemas and subschemas
A schema is a description of the entire database, i.e. the contents of records and the links between them.

12. Example of a Schema Customer(Customer ID, Name, Address)
Order(Order No. , Customer ID, Invoice No. , Status)
Invoice(Invoice No. , Customer ID, Order No. , Status)
Product(Product Code, Description)
Bold underline: primary key Underline: foreign key

13. Schemas and subschemas
Schema = a description of the structure of an entire database, used by database software to maintain the database
Subschema = a description of only that portion of the database pertinent to a particular user’s needs, used to prevent sensitive data from being accessed by unauthorised personnel

14. Database management systems (DBMS)
Database Management System = a software layer that maintains a database and manipulates it in response to requests from applications
Distributed Database = a database stored on multiple machines
DBMS will mask this organisational detail from its users
Data independence = the ability to change the organisation of a database without changing the application software which uses it

15. Figure 9.2 The conceptual layers of a database implementation
Two software layers: application software (user interface)
DBMS (database manipulation)

16. Advantages of the Conceptual Layers
Application software is simplified as it is insulated from database management details.
Easier to control user access through a single DBMS.
Data independence: database organisation can be changed without changing the application software.
to make a change in the DB for one user, need to change only the overall schema and the subschemas of users involved in the change.

17. Database models Database model = conceptual view of a database
Relational database model
Object-oriented database model
Abstraction
actual storage details are hidden from the user

18. 9.2 Relational database model
Name of relation
Customers
Address
Name
Customer no.
Table
heading
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Table
All data is stored in rectangular tables called relations.
Each row in a relation is a tuple
cardinality = no.of tuples
Each column is an attribute
degree = no. of attributes

19. Advantages of this Relational Model
Information is preserved.
e.g. the name and address of a customer are recorded even if the customer has no orders.
Information is not duplicated:
e.g. the name and address of a customer with two orders are not entered twice.

20. Figure 9.3 A relation containing employee information

21. A relation does not specify the order of the attributes or the order of the tuples.
Customers
Customers
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Univac
15 Retail Road
103
Honeywell
205 North Street 54
Sperry
1 The Lane
102
is the
same as

22. Repeated Tuples A relation cannot have repeated tuples
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Not a relation in a
relational database.

23. Attributes in Different Relations
An attribute of a given relation is not an attribute of any other relation.
E.g. “Orders.customer no”
is a different attribute from
“Customers.customer no”.

24. Keys
Primary key: an attribute whose value uniquely identifies a tuple.
Composite key: a minimal set of attributes whose values together uniquely identify a tuple
Foreign key: set of attributes pointing to a primary key or a composite key in another table.

25. Example of a Composite Key
Post Code
Town
Street
House no.
Composite key:
House no.,
Post Code.
CH3 8PN
Chester
High St. 23
DB21 0TQ
Bury
Acacia Ave. 1

26. Example of a Foreign Key
Product
Due date
Price
Customer no.
Order no.
Address
Name
Customer no.
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
Order file
Customer file

27. Evaluating a relational design
Avoid multiple concepts within one relation
Can lead to redundant data
Deleting a tuple could also delete necessary but unrelated information

28. Table Structure
Each table should correspond to a single concept or task.
Each row of a table should be uniquely identified by a key.
Avoid multiple copies of information.

29. Improving a relational design
Decomposition = dividing the columns of a relation into two or more relations, duplicating those columns necessary to maintain relationships
Lossless or nonloss decomposition = a “correct” decomposition that does not lose any information
Aim: to produce a better table structure

30. Example of a Lossless Decomposition
Address
Name
Product
Due date
Price
Customer no.
Order no.
205 North Street
Honeywell
Panel
2.4.06
£3400 54 20
25 Retail Road
Univac
Case
5.5.06
£2000
103 43
1 The Lane
Sperry
Plate
1.7.06
£1000
102 37
Original table

31. Lossless Decomposition
Address
Name
Customer no.
Product
Due date
Price
Customer no.
Order no.
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
Order file
Customer file

32. Example of a Lossy Decomposition
Dept
Job Title
Empl Id
Planning
Manager 18
Finance 17
Assistant
203

33. Lossy Decomposition
Dept
Job Title
Job Title
Empl Id
Planning
Manager
Finance
Assistant
Manager 18 17
Assistant
203
After decomposition we can no longer tell in which department employees 17 and 18 worked

34. Figure 9.7 A relation and a proposed decomposition

35. Figure 9.4 A relation containing redundancy
Problem: more than one concept has been combined in a single relation. Redesign the system with 3 relations...

36. Figure 9.5 An employee database consisting of three relations

37. Figure 9.6 Finding the departments in which employee 23Y34 has worked

38. Rules for database normalisation

39. Relational operations
Extracting information from a database
Can the answers to database queries be found in a
systematic way?
In the relational model the answers are always relations.

40. Example Database queries
Get
List of Order no and Product.
List of tuples of orders for Plate.
List of Order no and Name.

41. Contents of a Relational Database
Customers
Orders
Address
Name
Customer no
Product
Due date
Price
Customer no
Order no
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
All data in a relational database are stored in tables

42. Query 1: List of Order Nos. and Products
Project: remove
the attributes
Customer no., Price and Due
date to leave...
Orders
Product
Due date
Price
Customer no
Order no
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
Product
Order no
Ans1:
Panel 20
Case 43
Plate 37

43. Query 2: List of Tuples of Orders for Plate
Select: take all tuples
for which the product
is Plate, to give...
Product
Due date
Price
Customer no
Order no
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
Ans2:
Product
Due date
Price
Customer no
Order no
Plate
1.7.06
£1000
102 37

44. Query 3: List of Order No. and Name
Customers
Orders
Address
Name
Customer no
Product
Due date
Price
Customer no
Order no
205 North Street
Honeywell 54
15 Retail Road
Univac
103
1 The Lane
Sperry
102
Panel
2.4.06
£3400 54 20
Case
5.5.06
£2000
103 43
Plate
1.7.06
£1000
102 37
Join: combine tuples in Orders and Customers, then
use Project to retain the attributes Order no. and Name.

45. Query 3: List of Order No. and Name (cont'd)
Join and then Project
Temp <- Join Orders and Customers where O.Cno=C.Cno
C.add
C.Nm
C.Cno
O.Pr
O.Due
O.pr
O.Cno
O.no
205 North Street
Honeywell 54
Panel
2.4.06
£3400 20
15 Retail Road
Univac
103
Case
5.5.06
£2000 43
1 The Lane
Sperry
102
Plate
1.7.06
£1000 37
Ans3
C.Nm
O.no
Honeywell 20
Univac 43
Sperry 37
Names of attributes are abbreviated

46. Project
The original relation is R1, the new relation created by Project is R2 and the attributes projected to R2 are att1,…, attn.
R2 <- Project att1,…attn from R1
Example:
Ans1 <- Project Orders.Order no, Orders.Product from Orders

47. Figure 9.9 The PROJECT operation

48. Select
The original relation is R1, the new relation created by Select is R2 and the conditions on the attribute values in R2 are c1,…cn.
R2 <- Select from R1 where c1,…, cn.
Example:
Ans2 <- Select from Orders where Orders.Product=‘Plate’

49. Figure 9.8 The SELECT operation

50. Join
The original relations are R1, R2, the new relation created by Join is R3 and the conditions on the attribute values in R3 are c1,…cn.
R3 <- Join R1 and R2 where c1,…, cn
Example:
Temp <- Join Orders, Customers where O.Cno=C.Cno

51. Figure 9.10 The JOIN operation

52. Figure 9.11 Another example of the JOIN operation

53. Another Example for Join
R3  Join Orders and Customers where Orders.Price>£1500 and O.Cno=C.Cno R3
C.add
C.Nm
C.Cno
O.Pr
O.Due
O.pr
O.Cno
O.no
205 North Street
Honeywell 54
Panel
2.4.06
£3400 20
15 Retail Road
Univac
103
Case
5.5.06
£2000 43

54. Join and Keys A B C  Join A and B where A.foreign key=B.key C data22
DB1 16 D2 22 28 D1 16 34
C  Join A and B where A.foreign key=B.key
B.data
B.key
A.data
A.Foreign key
A.key C
DB2 22 D2 28
DB1 16 D1 34

55. Structured Query Language (SQL)
operations to manipulate tuples
insert
update
delete
select

56. Structured Query Language
SQL is used to retrieve data and manipulate data.
SQL is declarative. It describes the required information. It does not specify how the information is obtained.
Adopted by ANSI (1986) and ISO (1987).
First commercial release: Oracle Corporation (1979), then IBM (1979)

57. SQL Data Retrieval 1 List of Order no and Product.
select Order no, Product
from Orders
SQL command
The attributes Order no, Product
are projected from Orders
Result

58. SQL Data Retrieval 2 List of tuples of orders for Plate. select *
from Orders
where Orders.Product=‘Plate’
SQL command
* Means all attributes. The resulting
relation contains all tuples in Orders
for which Orders.Product=‘Plate’.
Result

59. SQL Data Retrieval 3 List of Order no and Name.
select Orders.Order no, Customers.name
from Orders, Customers
where Orders.Customer no = Customers.Customer no
SQL
command
Join Orders and Customers. Project the
the attributes Orders.Order no,
Customers.name
Result

60. SQL Command for Data Retrieval
select <list of attributes>
from <list of relations>
where <list of conditions>
select *
from Orders, Customers
where Orders.Price > 1500 and
Orders.Customer no=Customers.Customer no
Example

61. SQL Commands for Data Manipulation
insert into Orders
values (56, 54, ‘£1000’, ‘1.8.06’, ‘Plate’)
delete from Orders
where Orders.Customer no = 54
update Customers
set Customers.Address = ‘16 High Street’
where Customers.Customer no = 102

62. Advantages of SQL
High level language with short but powerful commands.
Simple, natural syntax.
Details of information retrieval left to the DBMS, allowing quick applications development.
Multipurpose: querying, modifying, controlling access to data, defining the database

63. Problems with SQL Large, complicated standard.
Standard does not always fully specify the effects of a command.
There are many commercial extensions of and variations on the standard, thus SQL code is usually not easily portable.

64. SQL and Relational Databases
SQL allows many deviations from the relational model, including:
Duplicate tuples
Unnamed attributes
Two or more attributes with the same name
Specification of the order of attributes

65. 9.3 Object-oriented databases
Object-oriented database = a database constructed by applying the object-oriented paradigm
Each data entity stored as a persistent object
Relationships indicated by links between objects
DBMS maintains inter-object links

66. Figure 9.13 The associations between objects in an object-oriented database

67. Advantages of object-oriented databases
Matches design paradigm of object-oriented applications
Intelligence can be built into attribute handlers
Example: names of people
Can handle exotic data types
Example: multimedia
Can store intelligent entities

68. 9.4 Maintaining database integrity
Transaction = a sequence of operations that must all happen together
Example: transferring money between bank accounts
Transaction log = non-volatile record of each transaction’s activities, built before the transaction is allowed to happen
Commit point = point at which transaction has been recorded in log
Roll-back = procedure to undo a failed, partially completed transaction
Problem of cascading rollback.

69. Maintaining database integrity (continued)
Simultaneous access problems
Incorrect summary problem
Lost update problem
Locking = preventing others from accessing data being used by a transaction
Shared lock: used when reading data
Exclusive lock: used when altering data

70. 9.5 Traditional File Structures
Sequential Files
Indexed Files
Hash Files

71. Sequential files
Sequential file = file whose contents can only be read in order
Reader must be able to detect end-of-file (EOF)
Data can be stored in logical records, sorted by a key field
Greatly increases the speed of batch updates

72. Figure 9.14 A procedure for merging two sequential files

73. Figure Applying the merge algorithm (Letters are used to represent entire records.  The particular letter indicates the value of the record’s key field.)

74. Figure 9.16 The structure of a simple employee file implemented as a text file

75. Indexed files Index = list of (key, location) pairs
Sorted by key values
location = where the record is stored

76. Figure 9.17 Opening an indexed file

77. Hashing Each record has a key The master file is divided into buckets
A hash function computes a bucket number for each key value
Each record is stored in the bucket corresponding to the hash of its key

78. Figure 9.18 Hashing the key field value 25X3Z to one of 41 buckets

79. Figure 9.19 The rudiments of a hashing system

80. Collisions in Hashing
Collision = when two keys hash to the same bucket
Major problem when table is over 75% full
Solution: increase number of buckets and rehash all data

81. 9.6 Data mining
Data mining = a set of techniques for discovering patterns in collections of data
Relies heavily on statistical analyses
Data warehouse = static data collection to be mined
Data cube = data presented from many perspectives to enable mining
Raises significant ethical issues when it involves personal information

82. Data mining strategies
Class description
Class discrimination
Cluster analysis
Association analysis
Outlier analysis
Sequential pattern analysis

83. Data mining strategies
Class description
identifying properties that characterise a given group of data items
what characterises people who buy small economical cars, or expensive mobile phones
Class discrimination
identifying properties that divide two groups
what distinguishes customers who buy used cars from those who buy new ones; or what distinguishes a reader of Vatan from a reader of Cumhuriyet

84. Data mining strategies
Cluster analysis
trying to discover classes
find properties of data items that identify groupings
Association analysis
looking for links between data groups
do people who buy beer also buy potato chips?

85. Data mining strategies
Outlier analysis
identify data entries that are out of the ordinary
e.g. identifying use of stolen credit cards from altered spending patterns
Sequential pattern analysis
try to identify patterns of behaviour over time
e.g. economic trends, climate conditions...

86. Social impact of database technology
Problems
Massive amounts of personal data are being collected
Often without knowledge or meaningful consent of affected people
Data merging produces new, more invasive information
Errors are widely disseminated and hard to correct
Remedies
Existing legal remedies largely ineffective
Negative publicity may be more effective