feapresfeapres supervisor: mr. le thanh quang. outlineoutline i. group introduction ii. capstone...
TRANSCRIPT
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FeApResFeApResFeApResFeApRes
Supervisor: Mr. Le Thanh Supervisor: Mr. Le Thanh QuangQuang
Supervisor: Mr. Le Thanh Supervisor: Mr. Le Thanh QuangQuang
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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
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I.Group IntroductionI.Group IntroductionI.Group IntroductionI.Group Introduction
PMTester/QA
DEV team
Supervisor
Mr. Le Thanh Quang
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II.Capstone Project II.Capstone Project IntroductionIntroduction
II.Capstone Project II.Capstone Project IntroductionIntroduction
Love
Passion
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II.Capstone Project II.Capstone Project IntroductionIntroduction
II.Capstone Project II.Capstone Project IntroductionIntroduction
Cook
Serve
Decorat
e
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II.Capstone Project II.Capstone Project IntroductionIntroduction
II.Capstone Project II.Capstone Project IntroductionIntroduction
Cook
Serve
Decorat
e
Restaurant
➣ Interesting but sophisticated business
➣ Tons of stuff to face with
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II.Capstone Project II.Capstone Project IntroductionIntroduction
II.Capstone Project II.Capstone Project IntroductionIntroduction
User requirements must be thoroughly analyzed
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II.Capstone Project II.Capstone Project IntroductionIntroduction
II.Capstone Project II.Capstone Project IntroductionIntroduction
Limitations of existing system:
Non user-friendly interface
Non VIP customer management supporting
Non data analyzing tool
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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.
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Requirement SpecificationRequirement SpecificationRequirement SpecificationRequirement SpecificationProblem:
Problem
QUẢN LÝ KHÁCH HÀNG
TÍCH ĐIỂM GIẢM GIÁ PHÂN TÍCH
DỮ LIỆU
TÀI SẢN
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Requirement SpecificationRequirement SpecificationRequirement SpecificationRequirement SpecificationScope:
TÍCH ĐIỂM GIẢM GIÁ PHÂN TÍCH
DỮ LIỆU
QUẢN LÝ KHÁCH HÀNG
TÀI SẢN
![Page 12: FeApResFeApRes Supervisor: Mr. Le Thanh Quang. OutlineOutline I. Group Introduction II. Capstone Project Introduction III. Software Project Plan IV. Software](https://reader034.vdocuments.net/reader034/viewer/2022051401/56649e175503460f94b025e4/html5/thumbnails/12.jpg)
Cook
Serve
Decorat
e
Requirement SpecificationRequirement SpecificationRequirement SpecificationRequirement Specification
Using Felica card and Apriori algorithm in restaurant
management
Solution
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II.Capstone Project II.Capstone Project IntroductionIntroduction
II.Capstone Project II.Capstone Project IntroductionIntroduction
Felica: contactless smart card system
Apriori algorithm: a classic algorithm for learning association rules
VIP customer management with card
Data mining
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III.Project PlanIII.Project PlanIII.Project PlanIII.Project Plan
DEVELOPMENT MODEL
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Development ModelDevelopment ModelDevelopment ModelDevelopment Model
Reasons
Time constraint
On time, accepted quality
Specific phases
Equal weight: coding, testing
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III.Project PlanIII.Project PlanIII.Project PlanIII.Project Plan
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Team WorkingTeam WorkingTeam WorkingTeam Working
Online Offline
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IV.Software Design IV.Software Design DescriptionsDescriptionsIV.Software Design IV.Software Design DescriptionsDescriptions
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Architecture DesignArchitecture DesignArchitecture DesignArchitecture Design
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Component designComponent design
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Database Design
Database Design
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Felica CardFelica Card
•Felica has high speed communication
•Felica has high security
•Felica is good at anti-collision
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OutlineOutline
I. Device
II. Communication Protocol
III. File System
IV. Commands
I. Device
II. Communication Protocol
III. File System
IV. Commands
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DeviceDevice
Reader / Writer Felica Card
Pasori (RC – S600) RC – S885
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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.
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Communication ProtocolCommunication Protocol
Application layer
Command Package
•Response Package
Application layer
Command Package
•Response Package
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File SystemFile System
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CommandsCommands
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DemoDemoUsing Felica Card in Restaurant
ManagementUsing Felica Card in Restaurant
Management
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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.
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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)
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)
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1. Association Rules Overview
1. Association Rules Overview
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
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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
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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.
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3. Apriori Algorithms3. Apriori Algorithms
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.
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3. Apriori Algorithms3. Apriori Algorithms
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3. Apriori Algorithms 3. Apriori Algorithms
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3.1. Apriori Algorithms : Step1
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
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3.1. Apriori Algorithms : Step1
3.1. Apriori Algorithms : Step1
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
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3.1.Apriori Algorithms : Step1
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
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3.2.Apriori Algorithms : Step2
3.2.Apriori Algorithms : Step2
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4. IMPROVEMENT4. IMPROVEMENT
4.1. Segmental Values (Mờ hóa dữ liệu)
4.2. Get Support (Speed up algorithms)
4.3. Weight Rules (Find important rules)
4.1. Segmental Values (Mờ hóa dữ liệu)
4.2. Get Support (Speed up algorithms)
4.3. Weight Rules (Find important rules)
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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, ….)
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4.1.Segmental Values4.1.Segmental Values
Fuzzy Set
• Triangle Function
Fuzzy Set
• Triangle Function
0
1
a b c
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4.1.Segmental Values4.1.Segmental Values
Fuzzy Set
• Trapezoid Function
Fuzzy Set
• Trapezoid Function
0
1
a b c d
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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
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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
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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)
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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
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DemoDemoUsing Apriori Algorithm in Restaurant
ManagementUsing Apriori Algorithm in Restaurant
Management
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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
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Test PlanTest Plan
Create test plan
Create and update test
cases
Execute test
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Test ExecutionTest Execution
1. Features to be tested• Authentication• Manager• Cashier• Accountant• Apriori algorithm
2. Features not to testedNone
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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.
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Algorithm TestingAlgorithm Testing
Algorithm’s speed:
- Hundreds of records (Order and Bill): ~ 3seconds
- Thousands of records : ~ 5seconds
- 100000 records : ~ 7,8 seconds
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Test ResultTest Result
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DemoDemoOther Business Functions in Restaurant
ManagementOther Business Functions in Restaurant
Management
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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
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Future FeaturesFuture Features
3. Improve Apriori algorithm
4. Build restaurant system including hardware and software support
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Lessons LearnedLessons Learned
Communication
Working in group
Felica technology
Apriori algorithm
Project management
Restaurant business domain
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FeApRes