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, XMLDesign tools for Info Sys: UML

API’s for mobile appsSecurity, CryptographyIS product lifecyclesAlgorithm analysis, P, NP, NPC

Info storage (modeling, normalization)Info retrieval (Relational algebra, Calculus, SQL)DB integrated API’s

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.

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

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

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

addresstown

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

ER models: relationships

A relationship is an association between two or more entities.

e.g. a customer Hayes borrows the loan L-15

Jones 321-12-3123 Main Harrison

Smith 019-28-3746 North Rye

Hayes 677-89-9011 Main Harrison

Williams 963-96-3963 Nassau Princeton

Adams 335-57-7991 Spring Pittsfield

L-17 1000

L-23 2000

…

L-11 900

L-16 1300

Entity set: customer Entity set: loans

Relationship set: borrows

customer

ssn name address

loan

loan_no amount

borrows

Relationship sets: participation (aka: existence dependency)

Jones 321-12-3123 Main Harrison

Smith 019-28-3746 North Rye

Hayes 677-89-9011 Main Harrison

Williams 963-96-3963 Nassau Princeton

Adams 335-57-7991 Spring Pittsfield

L-17 1000

L-23 2000

…

L-11 900

L-16 1300

Entity set: customer Entity set: loansborrows

Entity ‘Jones’ of type customer participates in relationship ‘borrows’

Total participation: if each entity of some set has at least one relationshipof 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’

Cardinality refers to how many of entities of a set can be related toto another entity in a relationship set.

Relationship sets: cardinality

customer

ssn name address

loan

loan_no amount

borrowsn m

m:n cardinality:Each customer may borrow more than one, say m, loansEach loan may be held by more than one, say n, customers.

1:n cardinalitye.g.: Each employee can have at most one manager (manager is also an employee)

employee managesn

1

1:1 cardinalitye.g.: Each branch can have at most one manager, and each employee can manage at most 1 branch

ER models: Superkeys, Candidate Keys

A set of attributes whose values can uniquely identify an entity of agiven 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

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 formcategories, or specializations.

account

account_no balance

isa

savings

interest-rate

checking

overdraft

isa

standard

interest-rate

gold

interest-payment min-balance

ER diagram notations

isa

Regular entity

Weak entity

simple attribute

multi-valued attribute

derived attribute

relationship

participation

total participation

specialization

Bank ER

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.

References and Further Reading

Silberschatz, Korth, Sudarshan, Database Systems Concepts, McGraw Hill

Next: Relational model, Normalization