1. Introduction to the database systems (1)
- What is a database
- The main characters of a database
- The basic database design method
- The entity-relationship data model for application modeling
Concepts and Architecture
- Database schema, Schema evolution, Database state
- Working process with a database system
- Database system architecture
- Data independence concept
Sept. 2004
Yangjun Chen

2. Data modeling using ER-model (2) - Entity types - strong entities
- Entity-relationship model
- Entity types
- strong entities
- weak entities
- Attributes - attribute classification
- Relationships among entities
- Constraints
- cardinality constraints
- participation constraints
- ER-to-Relation-mapping
Sept. 2004
Yangjun Chen

3. - static hashing & dynamic hashing - hash function
Hashing technique (3)
- external hashing
- static hashing & dynamic hashing
- hash function
- mathematical function that maps a key to a bucket address
- collisions
collision resolution scheme
- open addressing
- chaining
- multiple hashing
- linear hashing
Sept. 2004
Yangjun Chen

4. - root, internal, leaf, subtree - parent, child, sibling
Multi-level index (4)
- tree
- root, internal, leaf, subtree
- parent, child, sibling
- balanced, unbalanced
- b+-tree
- splits on overflow; merge on underflow
- in practice it is usually 3 or 4 levels deep
- search, insert, delete algorithms
Sept. 2004
Yangjun Chen

5. Data modeling using Relational model Relational algebra (5)
- Relational Data Model
- relational schema, relations
- database schema, database state
- integrity constraints and updating
- Relational algebra
- select, project, join, cartesian product
- set operations:
union, intersection, difference
- division
Sept. 2004
Yangjun Chen

6. - select-from-where clause - group by, having, order by - update
SQL (6)
- DDL
- creating schemas
- modifying schemas
- DML
- select-from-where clause
- group by, having, order by
- update
- view
Sept. 2004
Yangjun Chen

7. ER-to-Relation mapping (7)
General process
1. Create a relation for each strong entity type
2. Create a relation for each weak entity type
3. For each binary 1:1 relationship choose an entity and include the other’s PK in it as an FK
4. For each binary 1:n relationship, choose the n-side entity and include an FK wrt the other entity.
5. For each binary M:N relationship, create a relation for the relationship
6. For each multi-valued attribute create a new relation
7. For each n-ary relationship, create a relation for the relationship
Sept. 2004
Yangjun Chen

8. ER-to-Relation mapping (7)
4 choices (for handling is-a relationship):
1. Create separate relations for the supertype and each of the subtypes.
2. Create relations for the subtypes only - each contains attributes from the supertype.
3. (disjoint subtypes) Create only one relation - includes all of the attributes for the supertype and all for the subtypes, and one discriminator attribute.
4. (overlapping subtypes) Create only one relation - includes all of the attributes for the supertype and all for the subtypes, and one logical discriminator attribute per subtype.
Sept. 2004
Yangjun Chen

9. - Inference rules for function dependencies - 1NF - First Normal Form
Normalization (8)
- Function dependency
We say a functional dependency exists between the attributes X and Y: X  Y, if, whenever a tuple exists with the value x for X, it will always have the same value y for Y.
- Inference rules for function dependencies
- 1NF - First Normal Form
The domain of an attribute must only contain atomic values.
Sept. 2004
Yangjun Chen

10. - 2NF - Second Normal Form A relation schema is in 2NF if
Normalization (8)
- 2NF - Second Normal Form
A relation schema is in 2NF if
(1) it is in 1NF and
(2) every non-key attribute must be fully functionally dependent on the primary key.
- 3NF - Second Normal Form
A relation schema is in 3NF if
(1) it is in 2NF and
(2) each non-key attribute must not be fully functionally dependent on another non-key attribute (there must be no transitive dependency of a non-key attribute on the PK)
Sept. 2004
Yangjun Chen

11. (a) X is a superkey of R, or (b) A is a prime attribute of R.
Normalization (8)
- Boyce Codd Normal Form, BCNF
Consider a different definition of 3NF, which is equivalent to the previous one.
A relation schema R is in 3NF if, whenever a function dependency X  A holds in R, either
(a) X is a superkey of R, or
(b) A is a prime attribute of R.
Removing (b), we have the definition for BCNF.
Sept. 2004
Yangjun Chen

12. Lossless Join (9) Lossless join - Decomposition of a relation schema
Emp_PROJ
SSN
PNUM
hours
ENAME
PNAME
PLOCATION
F = {SSN  ENAME, PNUM  {PNAME, PLOCATION},
{SSN, PNUM}  hours} R1 R2
SSN
ENAME
PNUM
PNAME
PLOCATION R3
SSN
PNUM
hours
Lossless join
Sept. 2004
Yangjun Chen

13. - Matrix transformation to check the lossless join property
- Matrix for a decomposition A1
SSN A2
ENAME A3
PNUM A4
PNAME A5
PLOCATION A6
hours
b11
b21
b31
b12
b22
b32
b13
b23
b33
b14
b24
b34
b15
b25
b35
b16
b26
b36 R1 R2 R3
- Matrix transformation to check the lossless join property
Sept. 2004
Yangjun Chen

14. Lossless Join (9) SSN  ENAME SSN ENAME R1 R2 R3 a1 a2 b13 b14 b15 b16
Sept. 2004
Yangjun Chen

15. Lossless Join (9) PNUM  {PNAME, PLOCATION} PNUM PNAME PLOCATION R1 R2
b13
b14
b15
b16
b21
b22 a3 a4 a5
b26 a1 a2 a3 a4 a5 a6
Sept. 2004
Yangjun Chen

16. Deductive Databases (10)
- A deductive database can be defined as an advanced database augmented with an inference system.
- well-formed formulas
- clause and Horn-clause
- Bottom-up and top-down evaluation of non-recursive queries.
- Naive and semi-naive evaluation of recursive queries.
Sept. 2004
Yangjun Chen

17. Hierarchical Databases (11)
- Hierarchical schema
- PCR relationship
- PCR occurrence and hierarchical occurrence
- Virtual PCR
- Data definition and data manipulation in hierarchical schema
Sept. 2004
Yangjun Chen