1. Academic Year 2014 Spring

2. MODULE CC3005NI: Advanced Database Systems “DATABASE ARCHITECTURE”
Academic Year 2014 Spring

3. Topics: Historical Developments Navigational Data Models
Non-navigational Data Model
Data Independence
Database Languages

4. Historical Developments:
Navigational Data Models:
Hierarchical Model
Network Model
Non-navigational Data Model:
Relational Model

5. Hierarchical Model: Developed in the 1960s
To manage large amounts of data for complex manufacturing projects such as Apollo rocket that landed on moon (1969)
Its basic logical structure is represented by an upside-down tree.
The hierarchical structure contains levels, or segments. A segment is equivalent of a file system’s record type.

6. Hierarchical Model:
Within the hierarchy, a higher layer is perceived as the parent of the segment directly beneath it, which is called the child.
The hierarchical model depicts a set of one-to-many (1:M) relationships between a parent and its children segments.
Each parent can have many children, but each child has only one parent.

7. Hierarchical Model:
Depends on every entity being subject to a higher one.
A simple example is genealogy (each parent can be identified from the child and vice versa).
Another example of a representation of hierarchy of data is a customer invoice system

8. Hierarchical Model:

9. Hierarchical Model:

10. Hierarchical views can differ between user group
Hierarchical Model:
Hierarchical views can differ between user group

11. Hierarchical Model- DRAWBACKS:
Data is stored in hierarchies physically.
Difficult to change structure once a particular hierarchy has been designed / formulated, making it less flexible to meet dynamic needs.
(e.g. in the customer invoice example: it's not possible to allow a single payment to be made for several invoices)
Unplanned (ad-hoc) queries are difficult to support;- it may require major restructuring of the hierarchy

12. Network Model:
Network Model was created to represent complex data relationships more effectively than Hierarchical Model, to improve database performance, and to impose a database standard.
User perceives the network database as a collection of records in 1:M relationships.
Unlike the Hierarchical Model, Network Model allows a record to have more than one parent.

13. Network Model:
The Network Model represents a more complex structure, allowing non-hierarchical structures
Within a model any record may have many immediate parents as well as many dependents, reflecting more real-world scenarios.

14. A network of data:- customer invoice/payment example;
Network Model:
A network of data:- customer invoice/payment example;

15. Network Model:

16. Network Model:

17. Network Model- DRAWBACKS:
Data is stored in linked sets physically.
Pointer technology is used to implement relationships (with overhead, performance issues).
Unplanned queries still difficult to support
Programmer must be aware of 'sets' (relationships between record types) and the structural changes.
Users have to 'navigate' through database (not a most user-friendly way to interact with the database).

18. Relational Model:
Relational Model was introduced in 1970 by E. F. Codd (of IBM) in his landmark paper “A Relational Model of Data for Large Shared Databanks”
Data model that represents data in form of tables or relation.

19. Relational Model:
Relational database model consists of following three components:
Data structure: Data are organized in form of tables or relations.
Data manipulation: Powerful operations such as SQL languages or Query-by-example, are used to manipulate data stored in database.
Data integrity: Business rules are specified to maintain integrity of data when they are manipulated.

20. Relational Model: Physical Properties
A relation consists of 1 or more columns and 0 or more rows.
A row is called a tuple.
Each relation is given a unique name.
Each column has a name unique within the relation.
Each row contains an instance of the data associated with the relation.
A relation with no rows is empty (contains no data), but still exists.

21. Relational Model: Logical Properties
Columns are unordered, left to right. This property is designed to preserve the independence of each column.
Rows are unordered, top to bottom. This is designed to preserve the independence of each row.
No row may be duplicated in a given relation. Uniqueness in a relation is guaranteed by the designation of a Primary Key for each relation.

22. Relational Model:
A Candidate Key is an attribute that uniquely identifies a row in that relation.
A Primary Key is a candidate key that has been selected to be unique identifier for each row.
Primary key values cannot be null, since they would then not identify a row.
Columns can be interchanged without changing the meaning or use of relation.
It makes no difference as whether to insert a new row in front or at end of table.

23. Relational Model:

24. ANSI/ SPARC Database Model:
ANSI – The American National Standards Institute
SPARC – Standards Planning and Requirements Committee
The ANSI/SPARC model is used as a general framework (benchmark) on which various architectural issues of databases can be discussed on a level-playing field.
However, this is not the only model, and not every database system matches its 'structure'.

25. 3 Level Architecture :
The objective of the 3-Level Architecture is to separate the users’ view,
It allows independent customized user views: Each user should be able to access the same data, but have a different customized view of the data. These should be independent: changes to one view should not affect others.
It hides the physical storage details from users: Users should not have to deal with physical database storage details.
The database administrator should be able to change the database storage structures without affecting the users’ views.
The internal structure of the database should be unaffected by changes to the physical aspects of the storage: For example, a changeover to a new disk.

26. ANSI/ SPARC Model – 3 Levels:
Consisting of 3 levels, with 3 schemas:
External Level (User View):
A collection of individual users' views of the database (database is seen by users) - External Schema
A user's view of the database describes a part of the database that is relevant to a particular user. It excludes irrelevant data as well as data which the user is not authorized to access.

27. ANSI/ SPARC Model – 3 Levels:
Conceptual Level:
'global' definition/description of database in its entirety ('union‘ of all users views) at the logical level. It deals with information structure/content - Conceptual Schema
The conceptual level is a way of describing what data is stored within the whole database and how the data is inter-related. The conceptual level does not specify how the data is physically stored.

28. ANSI/ SPARC Model – 3 Levels:
Conceptual Level (continued):
Some important facts about this level are:
DBA works at this level.
Describes the structure of all users.
Only DBA can define this level.
Independent of hardware and software.

29. ANSI/ SPARC Model – 3 Levels:
Internal Level:
The internal level involves how the database is physically represented on the computer system.
It describes how the data is actually stored in the database and on the computer hardware.
It deals with information format/physical storage - Internal Schema

30. ANSI/ SPARC Model – 3 Levels:

31. ANSI/ SPARC Model – 2 Mappings:
Mapping is a process of transforming requests and results between the levels in the ANSI/SPARC model.
Programs refer to an external schema, and are mapped by the DBMS to the internal schema for execution.
Data extracted from the internal DBMS level is reformatted to match the user’s external view.
There are 2 mappings:
external/conceptual mapping
conceptual/internal mapping

32. ANSI/ SPARC Model – 2 Mappings:
External-Conceptual Mapping:
An External-Conceptual Mapping defines the correspondence between a particular external view and the conceptual view.
It tells the DBMS which objects on the conceptual level correspond to the objects requested on a particular user's external view.
If changes are made to either an external view or conceptual view, then mapping must be changed accordingly.

33. ANSI/ SPARC Model – 2 Mappings:
Conceptual-Internal Mapping:
The Conceptual-Internal Mapping defines the correspondence between the conceptual view and the internal view, i.e. database stored on the physical storage device.
It describes how conceptual records are stored and retrieved to and from the storage device.
This means that conceptual-internal mapping tells the DBMS that how the conceptual! records are physically represented.
If the structure of the stored database is changed, then the mapping must be changed accordingly.

34. ANSI/ SPARC Model – 2 Mappings:
External/ Conceptual Mapping
Conceptual/ Internal Mapping

35. Data Independence:
The ability to allow users to take a logical view of the database which is independent of the way that the data is actually stored.
The ANSI/SPARC model based on the 3 schema architecture can be used to explain the concept of Data Independence (DI). Mappings are essential to DI.
Data Independence can be defined as the capacity to change the schema at one level of a database system without having to change schema at next higher level.

36. Data Independence:
This allows users to take a logical view of the database which is independent of the way that the data is actually stored.
There are two types of Data Independence
Logical Data Independence:
Logical data is data about database, that is, it stores information about how data is managed inside. For example, a table (relation) stored in the database and all constraints, which are applied on that relation.
Logical data independence is a kind of mechanism, which separates itself from actual data stored on the disk. If we do some changes on table format it should not change the data residing on disk.

37. Data Independence (continued):
Physical Data Independence:
All schemas are logical and actual data is stored in bit format on the disk. Physical data independence is the power to change the physical data without impacting the schema or logical data.
For example, in case we want to change or upgrade the storage system itself, that is, using SSD (Solid-State-Disk/ Drive) instead of Hard-disks should not have any impact on logical data or schemas.

38. Data Independence:
Different applications will need different views of same data
e.g. CUSTOMER BALANCE

39. Frontend/ Backend System:

40. Two Tier System Architecture:

41. Three Tier System Architecture:
(Java Database Connectivity)

42. Three Tier Approach - ADVANTAGES:
High Performance, lightweight bound objects
‘thin’ client (compared to the 2-tier architecture), with less expensive hardware
reduction in client-side administration
centralised application maintenance
enhanced modularity and tier independence;- easier to modify/replace one tier without affecting others

43. Three Tier Approach - ADVANTAGES:
High degree of flexibility in deployment platform and configuration
Improve Data Integrity
Improved Security – Client is not direct access to database
Improved load balancing of business logic, by separating core business logic from database functions.
An added advantage is that the 3-tier architecture maps quite naturally to the Web-enable database environment.

44. Web Enabled Database Architecture:

45. Thank you!!! Questions are WELCOME
Academic Year 2014 Spring