1. IELM 511: Information System design Introduction Part I. ISD for well structured data – relational and other DBMS Part II. ISD for systems with non-uniformly structured data Part III: (one out of) Basics of web-based IS (www, web2.0, …) Markup’s, HTML, XML Design tools for Info Sys: UML API’s for mobile apps Security, Cryptography IS product lifecycles Algorithm analysis, P, NP, NPC Info storage (modeling, normalization) Info retrieval (Relational algebra, Calculus, SQL) DB integrated API’s

2. Bank is organized in branches. Each branch is located in a particular city and identified by a unique name. The bank monitors the assets of each branch. Example: Banking system Customers are identified by their SSN (equiv to HKID). The bank stores each customer’s name and address. Customers may have accounts, and can take out loans. A customer may be associated with a particular banker, who may act as a loan officer of personal banker for that customer. Bank employees are also identified by SSN. The bank stores the Name, address, phone #, start day of employment of each employee, the name of all dependents of the employee, and the manager of the employee. The bank offers two types of accounts: savings and checking. Accounts can be held by more than one customer, and a customer may have many accounts. Each account has a unique account number. We store each account’s balance, and the most recent date when the account was accessed by each customer holding the account. Each savings account has an interest rate, and overdrafts are recorded for each checking account. A loan originates at a particular branch, and is held by one or more customers. Each loan has a unique number. For each loan, the bank stores the loan amount and the payments (date and amount). Payment numbers are not unique, but a payment number uniquely identifies a payment for a specific loan.

3. Information Storing: ER models Entity: a well defined real/abstract object in the domain of the IS. e.g. A particular customer of the bank; A specific loan; … Attributes: properties whose values describe the entity. e.g. Customer is described by attributes ‘SSN’, ‘Name’, ‘Address’ customer ssn name address

4. ER models: entity types Types of entities - Regular entity: one or a combination of attribute values uniquely identifies the entity in a set. - Weak entity: no combination of attribute values can uniquely identify the entity in a set. customer ssn name address Entity set: a set of entities of the same type. e.g. {[Jones, 321-12-3123, Main, Harrison],..., [Adams, 335-57-7991, Spring, Pittsfield]} loan-payment payment_no date amount

5. ER models: attribute types - Simple attribute The attribute has values that are atomic ssn - Composite attribute Attribute value is composed of 2 or more pieces - Single valued A given entity will only have one value for that attribute address town street - Multi-valued A unique entity may have multiple values for this attribute name - Derived attribute If the value of the attribute can be derived/computed from some other values dependent-name employment-length

6. ER models: relationships A relationship is an association between two or more entities. e.g. a customer Hayes borrows the loan L-15 Jones321-12-3123MainHarrison Smith019-28-3746NorthRye Hayes677-89-9011MainHarrison Williams963-96-3963NassauPrinceton Adams335-57-7991SpringPittsfield L-171000 L-232000 … L-11900 L-161300 Entity set: customer Entity set: loans Relationship set: borrows customer ssn name address loan loan_no amount borrows

7. Relationship sets: participation (aka: existence dependency) Jones321-12-3123MainHarrison Smith019-28-3746NorthRye Hayes677-89-9011MainHarrison Williams963-96-3963NassauPrinceton Adams335-57-7991SpringPittsfield L-171000 L-232000 … L-11900 L-161300 Entity set: customer Entity set: loans borrows Entity ‘Jones’ of type customer participates in relationship ‘borrows’ Total participation: if each entity of some set has at least one relationship of type ‘borrows’, then it has total participation in that relationship type. e.g. loans has total participation in ‘borrows’ Partial participation: if some entities of a set do not participate in a relation. e.g. customer has partial participation in ‘borrows’

8. Cardinality refers to how many of entities of a set can be related to to another entity in a relationship set. Relationship sets: cardinality customer ssn name address loan loan_no amount borrows n m m:n cardinality: Each customer may borrow more than one, say m, loans Each loan may be held by more than one, say n, customers. 1:n cardinality e.g.: Each employee can have at most one manager (manager is also an employee) employee manages n 1 1:1 cardinality e.g.: Each branch can have at most one manager, and each employee can manage at most 1 branch

9. ER models: Superkeys, Candidate Keys A set of attributes whose values can uniquely identify an entity of a given type is called a Superkey of that entity. e.g. {ssn} is a Superkey of entity customer. e.g. {name, address} is not a superkey of entity customer [Why?] customer ssn name address Notice: If K is a superkey, any superset of K is also a superkey. Any minimal superkey, K, is called a candidate key. minimal => removing any element from K will give subset that are not superkeys

10. ER models: Specializations Suppose all entities of a given set can be categorized further into a few subsets. e.g. entity account may be of type savings or checking. The subsets form categories, or specializations. account account_no balance isa savings interest-rate checking overdraft isa standard interest-rate gold interest-payment min-balance

11. ER diagram notations isa Regular entity Weak entity simple attribute multi-valued attribute derived attribute relationship participation total participation specialization

12. Bank ER

13. ER Diagrams: use Why bother to create a graphical image of the same data as text ? 1. Construction of ER model assists focusing on complete information 2. Easier to map ER model into relational model This relational model is a ‘good’ starting DB design.

14. References and Further Reading Silberschatz, Korth, Sudarshan, Database Systems Concepts, McGraw Hill Next: Relational model, Normalization