feapresfeapres supervisor: mr. le thanh quang. outlineoutline i. group introduction ii. capstone...

FeApResFeApResFeApResFeApRes

Supervisor: Mr. Le Thanh Supervisor: Mr. Le Thanh QuangQuang

Supervisor: Mr. Le Thanh Supervisor: Mr. Le Thanh QuangQuang

OutlineOutlineOutlineOutline

I. Group Introduction

II. Capstone Project Introduction

III. Software Project Plan

IV. Software Design Description

V. Demo

VI. Software Testing

I. Group Introduction

II. Capstone Project Introduction

III. Software Project Plan

IV. Software Design Description

V. Demo

VI. Software Testing

I.Group IntroductionI.Group IntroductionI.Group IntroductionI.Group Introduction

PMTester/QA

DEV team

Supervisor

Mr. Le Thanh Quang

II.Capstone Project II.Capstone Project IntroductionIntroduction


Love

Passion



Cook

Serve

Decorat

e



Cook

Serve

Decorat

e

Restaurant

➣ Interesting but sophisticated business

➣ Tons of stuff to face with



User requirements must be thoroughly analyzed



Limitations of existing system:

Non user-friendly interface

Non VIP customer management supporting

Non data analyzing tool

User RequirementsUser RequirementsUser RequirementsUser Requirements

Customer

Waiter/Waitress

Cashier

Manager

Accountant

FeApRes

The system has the domain

functions like other common

restaurant management systems.

Smart card to support

management.

Tool to analyze transaction data

for meaningful conclusion.

Requirement SpecificationRequirement SpecificationRequirement SpecificationRequirement SpecificationProblem:

Problem

QUẢN LÝ KHÁCH HÀNG

TÍCH ĐIỂM GIẢM GIÁ PHÂN TÍCH

DỮ LIỆU

TÀI SẢN

Requirement SpecificationRequirement SpecificationRequirement SpecificationRequirement SpecificationScope:

TÍCH ĐIỂM GIẢM GIÁ PHÂN TÍCH

DỮ LIỆU

QUẢN LÝ KHÁCH HÀNG

TÀI SẢN

Cook

Serve

Decorat

e

Requirement SpecificationRequirement SpecificationRequirement SpecificationRequirement Specification

Using Felica card and Apriori algorithm in restaurant

management

Solution



Felica: contactless smart card system

Apriori algorithm: a classic algorithm for learning association rules

VIP customer management with card

Data mining

III.Project PlanIII.Project PlanIII.Project PlanIII.Project Plan

DEVELOPMENT MODEL

Development ModelDevelopment ModelDevelopment ModelDevelopment Model

Reasons

Time constraint

On time, accepted quality

Specific phases

Equal weight: coding, testing

III.Project PlanIII.Project PlanIII.Project PlanIII.Project Plan

Team WorkingTeam WorkingTeam WorkingTeam Working

Online Offline

IV.Software Design IV.Software Design DescriptionsDescriptionsIV.Software Design IV.Software Design DescriptionsDescriptions

Architecture DesignArchitecture DesignArchitecture DesignArchitecture Design

Component designComponent design

Database Design

Database Design

Felica CardFelica Card

•Felica has high speed communication

•Felica has high security

•Felica is good at anti-collision

OutlineOutline

I. Device

II. Communication Protocol

III. File System

IV. Commands

I. Device

II. Communication Protocol

III. File System

IV. Commands

DeviceDevice

Reader / Writer Felica Card

Pasori (RC – S600) RC – S885

Communication ProtocolCommunication Protocol

Physical layer

defines the physical and electrical characteristics of data transfer

Data link layer

defines the data transfer method and the error detection scheme.

Application layer

defines the specifications and functions of data strings to be handled as commands.

Physical layer

defines the physical and electrical characteristics of data transfer

Data link layer

defines the data transfer method and the error detection scheme.

Application layer

defines the specifications and functions of data strings to be handled as commands.

Communication ProtocolCommunication Protocol

Application layer

Command Package

•Response Package

Application layer

Command Package

•Response Package

File SystemFile System

CommandsCommands

DemoDemoUsing Felica Card in Restaurant

ManagementUsing Felica Card in Restaurant

Management

Apriori AlgorithmsApriori Algorithms

Target:

Decision-making

Making strategic plan

E.g.:

Preparing inventory

Suggesting customer

Managing menu (add, remove dish when

needed)

Managing human resource.

OutlineOutline

1. Association Rules Overview

2. Apriori Overview

3. Apriori Algorithms

4. Improvement

4.1. Segmental Values (Mờ hóa dữ liệu)

4.2. Get Support (Speed up algorithms)

4.3. Weight Rules (Find important rules)


2. Apriori Overview

3. Apriori Algorithms

4. Improvement






Association Rule : Relations between variables in large databases.

E.g. (Sea food) => (Wine)

Algorithms for finding association rules:

o Apriori algorithm

o Eclat algorithm

o FP-growth algorithm

o One-attribute-rule

o Zero-attribute-rule

Association Rule : Relations between variables in large databases.

E.g. (Sea food) => (Wine)

Algorithms for finding association rules:

o Apriori algorithm

o Eclat algorithm

o FP-growth algorithm

o One-attribute-rule

o Zero-attribute-rule

2. Apriori Overview2. Apriori Overview

Best-known algorithm to mine association rules

Advantages

o Find all rules

o Simple

Disadvantages

o Suffer from a number of inefficiencies or trade-offs

o Operate in binary data only

Best-known algorithm to mine association rules

Advantages

o Find all rules

o Simple

Disadvantages

o Suffer from a number of inefficiencies or trade-offs

o Operate in binary data only

3. Apriori Algorithms3. Apriori Algorithms

Step 1: Find all frequent itemsets:

Get frequent items:

• Items whose occurrence in database is greater than or equal to the min support.

Get frequent itemsets:

• Generate candidates from frequent items.

• Use the candidate to find the frequent itemsets.

• Repeat until there are no new candidates.

Step 1: Find all frequent itemsets:

Get frequent items:

• Items whose occurrence in database is greater than or equal to the min support.

Get frequent itemsets:

• Generate candidates from frequent items.

• Use the candidate to find the frequent itemsets.

• Repeat until there are no new candidates.


Step 2: Generate strong association rules from frequent item sets

Rules which satisfy the min support and min confidence.

Step 2: Generate strong association rules from frequent item sets

Rules which satisfy the min support and min confidence.

3. Apriori Algorithms 3. Apriori Algorithms

3.1. Apriori Algorithms : Step1


Transaction

ACD

BCE

ABCE

BE

L1-Itemset Support

{A} 2

{B} 3

{C} 3

{E} 3

Min Support = 50 % Min Confidence = 80%

L2-Itemset Support

{AC} 2

{BC} 2

{BE} 3

{CE} 2

Self-Join

Check Support

Item Support

{A} 2

{B} 3

{C} 3

{D} 1

{E} 3

Check Support

Item Support

{AB} 1

{AC} 2

{AE} 1

{BC} 2

{BE} 3

{CE} 2



Self -Join

Check Support

L2

L3

All subsets of frequent Items

must be frequent.

{ABCDEF} must combine with itemsets like {ABCDEG}

Item Support

{BCE} 2

L2-Itemset Support

{AC} 2

{BC} 2

{BE} 3

{CE} 2

L3-Itemset Support

{BCE} 2

3.1.Apriori Algorithms : Step1


Frequent Items Support

{A} 2

{B} 3

{C} 3

{E} 3

{AC} 2

{BC} 2

{BE} 3

{CE} 2

{BCE} 2

4. IMPROVEMENT4. IMPROVEMENT







4.1.Segmental Values4.1.Segmental Values

Major disadvantage: binary database.

-> Convert conventional database to binary database

Value Types:

o Category values

o Continuous values (E.g. Age, money, ….)

Major disadvantage: binary database.

-> Convert conventional database to binary database

Value Types:

o Category values

o Continuous values (E.g. Age, money, ….)


Fuzzy Set

• Triangle Function

Fuzzy Set

• Triangle Function

0

1

a b c


Fuzzy Set

• Trapezoid Function

Fuzzy Set

• Trapezoid Function

0

1

a b c d

4.1. Segmental Values4.1. Segmental ValuesBill values (0-> max: integer)

• Low= F1(x,0,0,500,600) (red line)• Medium = F2(x,600,500,2000,2500) (blue line)• High = F3(x,2000,2500,MaxInt, MaxInt) (yellow line)• MinWT = 0.4

Bill values (0-> max: integer) • Low= F1(x,0,0,500,600) (red line)• Medium = F2(x,600,500,2000,2500) (blue line)• High = F3(x,2000,2500,MaxInt, MaxInt) (yellow line)• MinWT = 0.4

Example : if F1(2200) = 0; F2(F1(2200) = 0.5; F3(2200) = 0.6) => 2200k Bill of 2200 is considered as both Middle and High

4.2. Get Support4.2. Get Support

This procedure is the most time consuming part in the algorithms. This procedure is the most time consuming part in the algorithms.

L1-Itemset

Support

{A} 2

{B} 3

{C} 3

{E} 3

L2-Itemset

Support

{AC} 2

{BC} 2

{BE} 3

{CE} 2

Item Support

{AB} 1

{AC} 2

{AE} 1

{BC} 2

{BE} 3

{CE} 2

Join

Check Support

Item Support

{A} 2

{B} 3

{C} 3

{D} 1

{E} 3

Check Support

L1

L2

4.2. Get Support4.2. Get SupportTransaction

ACDE

BCE

ABCE

BCE

AB

SET Elements

A {1,3,5}

B {2,3,4,5}

C {1,2,3,4}

D {1}

E {1,2,3,4}

Need algorithms to calculate intersection of two sets (HASH SET)

4.3. Weight Rules 4.3. Weight Rules

• Rule: (Vip card = Yes & Time = Afternoon) => Bill Amount = High

• Some components are more interesting than others E.g.: Bill Amount

=> Each component is weighted.

• Importance of rule A=>B is

• Rule: (Vip card = Yes & Time = Afternoon) => Bill Amount = High

• Some components are more interesting than others E.g.: Bill Amount

=> Each component is weighted.

• Importance of rule A=>B is

DemoDemoUsing Apriori Algorithm in Restaurant

ManagementUsing Apriori Algorithm in Restaurant

Management

VI. TestingVI. TestingVI. TestingVI. Testing

Bases

The system including 5 components as: - Authentication - Accountant - Manager - Cashier - Apriori Algorithm

The stages of testing:- Unit Test- Integration Test & System Test- User Acceptance Test

Final SRS Detail Design

Test PlanTest Plan

Create test plan

Create and update test

cases

Execute test

Test ExecutionTest Execution

1. Features to be tested• Authentication• Manager• Cashier• Accountant• Apriori algorithm

2. Features not to testedNone

Test DataTest Data

Problem with real test data

1.Functionality testing:Create sample data in logic (about 10 records

for each table) and execute test.

2. Performance testing:Using SQL query to generate test data. They’re

created randomly.

Algorithm TestingAlgorithm Testing

Algorithm’s speed:

- Hundreds of records (Order and Bill): ~ 3seconds

- Thousands of records : ~ 5seconds

- 100000 records : ~ 7,8 seconds

Test ResultTest Result

DemoDemoOther Business Functions in Restaurant

ManagementOther Business Functions in Restaurant

Management

Future FeaturesFuture Features

1. Use Felica as the smart card- Check-in and check-out of staff management- Customer and supplier management

2. Complete all business functions of the system- Inventory management- Accounting- Online reservation

Future FeaturesFuture Features

3. Improve Apriori algorithm

4. Build restaurant system including hardware and software support

Lessons LearnedLessons Learned

Communication

Working in group

Felica technology

Apriori algorithm

Project management

Restaurant business domain

FeApRes

feapresfeapres supervisor: mr. le thanh quang. outlineoutline i. group introduction ii. capstone...

Documents

tool slide

anticollision slide

offline slide

project plan slide

software testing slide

file system slide

card data mining slide

database design slide