1. Introduction to Databases
Data Organisation
Definition
Data modelling
SQL
DBMS functions

2. Basics of data Organisation:
DATA HIERARCHY (four categories)
Fields = represent a single data item
Records = made up of a related set of fields describing one instance of an entity
File / Table = a set of related records - as many as instances (occurrence) in the set
Database = a collection of related files

3. Example of data structure
Fields
Name First name Telephone
Sampras Pete
Healy Margaret
Clinton Bill
Henry Thierry
Records
+ Other files =>complete data
Structure = DB
File / Table

4. Database: Definition.
"A collection of interrelated data stored together with controlled redundancy, to serve one or more applications in an optimal fashion; the data is stored so that it is independent of the application programs which use it; a common and controlled approach is used in adding new data and in modifying existing data within the database."

5. Definition - closer look
A collection of interrelated data stored together
with controlled redundancy
to serve one or more applications in an optimal fashion
the data is stored so that it is independent of the application programs which use it
a common and controlled approach is used in adding new data and in modifying existing data within the database.

6. Advantages of Databases:
data are independent from applications - stored centrally
data repository accessible to any new program
data are not duplicated in different locations
programmers do not have to write extensive descriptions of the files
These save enough money and time to offset the extra costs of setting and maintaining DBs

7. Disadvantages of DBs: Data are more accessible so more easily abused
Large DBs require expensive hardware and software
specialised / scarce personnel is required to develop and maintain large DBs
People / business units may object to “their” data being widely available in a DB

8. Characteristics of DBs…
High concurrency (high performance under load)
Multi-user (read does not interfere with write)
Data consistency – changes to data don’t affect running queries + no phantom data changes
High degree of recoverability (pull the plug test)

9. ACID test Atomicity Consistency Isolation Durability All or nothing
Preserve consistency of database
Transactions are independent
Once committed data is preserved

10. DataBase Management System (DBMS):
program that makes it possible to:
create
use
maintain a database
It provides an interface / translation mechanism between the logical organisation of the data stored in the DB and the physical organisation of the data

11. Using a database: Two main functions of the DBMS :
Query language - for people who are not programmer (greatest advantage of DB)
Data manipulation language - for programmers who want to modify the links between data elements within the DB
Also, Host Language - the language used by programmers to develop the rest of the application - eg: Visual Basic for Applications (VBA) / Oracle developer 2000

12. Different types of DBs:
creating the DB = specifying the links between data items
different types of relationships can be specified - ie different logical views
they correspond to three main types of DBMSs:
Hierarchical DBs
Network DBs
Relational DBs
Object Oriented DBs

13. Hierarchical DBs:
data item are related as “Parent” and “Child” in a tree-like structure
“parent” means data item is higher in the tree than “child” and connected to it
one “parent” can have more than one “child”, but one “child” can only have one “parent”
most common platform = IBM’s Information Management System (IMS)

14. Example Customers Payments Orders Currency Items Unit of packaging
Substitution Product
Very fast retrieval

15. Undesirable side effects:
Insertion of record:
dependent record cannot be added without a parent
eg: units of packaging cannot be added without linkage to an existing item
Deletion of record:
deletion of a parent deletes all children
deleting an existing item will delete its replacement items
Impossible to have two parents = trouble

16. Network DBs:
same as parent and children in Hierarchical DB, but children can have more than one parent
It is also possible to link items upwards to other items parents
practically, it means that the DBMS is more flexible for data retrieval

17. Example Suppliers Customers Payments Orders Currency Items
Unit of packaging
Substitution Product

18. Relational DBs: Data items stored in tables
Specific fields in tables related to other field in other tables (joint)
infinite number of possible viewpoints on the data (queries)
Highly flexible DB but overly slow for complex searches
Oracle, SyBase, Ingres, Access, Paradox for Windows...

19. Describing relationships
Attempt at modelling the business elements (entities) and their relationships (links)
Can be based on users’ descriptions of the business processes
Specifies dependencies between the data items
Coded in an Entity-Relationship Diagram (ERD)

20. Types of Relationships
one-to-one: one instance of one data item corresponds to one instance of another
one-to-many: one instance to many instances
many-to-many: many instance correspond to many instances
Also some relationships may be:
compulsory
optional

21. Example Student registering system What are the entities?
What type of relationship do they have?
Draw the diagram

22. Entity Relationship Diagram

23. Next step - creating the data structure
Few rules - a lot of experience
Can get quite complex (paramount for the speed of the DB)
Tables must be normalised - ie redundancy is limited to the strict minimum by an algorithm
In practice, normalisation is not always the best

24. Data Structure Diagrams
Describe the underlying structure of the DB: the complete logical structure
Data items are stored in tables linked by pointers
attribute pointers: data fields in one table that will link it to another (common information)
logical pointers: specific links that exist between tables
Tables have a key
If an attribute seems to belong to a relationship rather than an attribute, it may mean an associative entity must be added

25. * compulsory attributes 0 optional attributes
ORDER
order number
Item description
Item Price
Quantity ordered
Customer number
Item number
Customer
Customer number
Customer name
Customer address
Customer balance
Customer special rate 1 2 3 4
Item
Item number
Item description
Item cost
Quantity on hand
* compulsory attributes
0 optional attributes

26. Definitions Entity Attributes Instance(s) Domain
Key (candidate primary and foreign)

27. Definitions
Relationship
Ordinality
Cardinality
Associative Entity

28. Some test questions Is it a bird is it a plane?
Is it an entity or an attribute?

29. Normalisation
Process of simplifying the relationships amongst data items as much as possible (see example provided - handout)
Through an iterative process, structure of data is refined to 1NF, 2NF, 3NF etc.
Reasons for normalisation:
to simplify retrieval (speed of response)
to simplify maintenance (updates, deletion, insertions)
to reduce the need to restructure the data for each new application

30. First Normal Form
design record structure so that each record looks the same (same length, no repeating groups)
repetition within a record means one relation was missed = create new relation
elements of repeating groups are stored as a separate entity, in a separate table
normalised records have a fixed length and expanded primary key

31. Second Normal Form Record must be in first normal form first
each item in the record must be fully dependent on the key for identification
Functional dependency means a data item’s value is uniquely associated with another’s
only on-to-one relationship between elements in the same file
otherwise split into more tables

32. Third normal form to remove transitive dependencies
when one item is dependent on an item which is dependent from the key in the file
relationship is split to avoid data being lost inadvertently
this will give greater flexibility for the design of the application + eliminate deletion problems
in practice, 3 NF not used all the time - speed of retrieval can be affected

33. Beyond data modeling Model must be normalised – purpose ?
Outcome is a set of tables = logical design
Then, design can be warped until it meets the realistic constraints of the system
Eg: what business problem are we trying to solve? – see handout [riccardi p. 113, 127]
Update anomalies
Each item should appear only once
+ you ask many good questions

34. Realistic constraints
Users cannot cope with too many tables
Too much development required in hiding complex data structure
Too much administration
Optimisation is impossible with too many tables
Actually: RDBs can be quite slow!

35. Key practical questions
What are the most important tasks that the DB MUST accomplish efficiently?
How must the DB be rigged physically to address these?
What coding practices will keep the coding clean and simple?
What additional demands arise from the need for resilience and security?

36. Analysis - Three Levels of Schema
External Schema 1
External Schema 2
External Schema …
Tables
Logical Schema
Disk
Array
Internal Schema

37. 4 way trade-off
Security
Performance
Ease of use
Clarity of code

38. Key decisions Oracle offers many different ways to do things
Indexes
Backups…
Good analysis is not only about knowing these => understanding whether they are appropriate
Failure to think it through => unworkable model
Particularly, predicting performance must be done properly
Ok on the technical side, tricky on the business side

39. Design optimisation Sources of problems:
Network traffic
Excess CPU usage
But physical I/O is greatest threat (different from physical I/O)
Disks still the slowest in the loop
Solution: minimise or re-schedule access
Also try to minimise the impact of Q4 (e.g. mirroring, internal consistency checks…)

40. Creating links between the tables
use common fields to join tables / queries
very easy when data is properly normalised
Gives total flexibility in terms of data retrieval
Main strength of RDBs (SQL)

41. Structured Query Language
used for defining and manipulating data in Relational DBs
aimed at:
reducing training costs
increasing productivity
improve application portability
increase application longevity
reduce dependency on single vendors
enable cross systems communication
In practice, SQLs can be a bit different

42. Querying RDBs with SQL
use a form of pseudo english to retrieve data in a view (which looks like a table)
syntax is based on a number of “clauses”
Select: specifies what data elements will be included in the view
From: lists the tables involved
Where: specifies conditions to filter the data
specific values sought
links between tables

43. Example with one table
find the name and address of customer number 1217

44. Example with a range
find the items which are priced between £50 and £15000

45. Example with two tables
find the rep name of all customers

46. Example with two tables
same for customer Robson only

47. Use of a Search Condition - nested queries
find the name and address of the customer who ordered order # 110

48. Additional syntax Add computation in the “select” statement:
select SUM(price)
select AVG(price), MAX, MIN, COUNT
Simplify comparisons with a BETWEEN clause and LIKE clause (with *, ?)
Add sorting instruction after the where clause
ORDER BY name (alphabetical)
ORDER BY price (ascending)
Provide aggregate information by grouping data:
GROUP BY customer

49. find contents (item# and description) of order 110:

50. find the average price of the cars for sale
find the average price of all orders taken so far by customer “Jones”

51. find how much cash customer “Barry” has generated in total

52. find the average price of all orders taken so far

54. Sales Order Processing
Oracle Demo Set -
Sales Order Processing
CUSTOMER TABLE
SALES_ORDER TABLE
PRODUCT TABLE
ITEM TABLE
PRICE TABLE

55. Oracle Demo Set - Employee Data
DEPARTMENT TABLE
EMPLOYEE TABLE
LOCATION TABLE
JOB TABLE
Oracle Demo Set -
Employee Data