1. FeApResFeApRes Supervisor: Mr. Le Thanh Quang

2. OutlineOutline 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

3. I.Group Introduction PMTester/QA DEV team Supervisor Mr. Le Thanh Quang

4. II.Capstone Project Introduction L ove P assion

5. II.Capstone Project Introduction Cook Serve Decorate

6. II.Capstone Project Introduction Cook Serve Decorate Restaurant ➣ Interesting but sophisticated business ➣ Tons of stuff to face with

7. II.Capstone Project Introduction Limitations of existing system non user-friendly interface no setting for access permission no customer management

8. II.Capstone Project Introduction User requirements must be thoroughly analyzed

9. II.Capstone Project Introduction Customer Waiter/Waitress Cashier Manager Accountant FeApRes Data can be backed up Cashier can manage orders, bills Manager can manage accounts, menu, inventory, reservation and reports Accountant can manage reports Be able to manage VIP customers

10. II.Capstone Project Introduction Problem QU Ả N LÝ KHÁCH HÀNG TÍCH Đ I Ể M GI Ả M GIÁ PHÂN TÍCH DỮ LIỆU Problem

11. II.Capstone Project Introduction Scope QU Ả N LÝ KHÁCH HÀNG TÍCH Đ I Ể M GI Ả M GIÁ PHÂN TÍCH DỮ LIỆU

12. Cook Serve Decorate II.Capstone Project Introduction Using Felica card and Apriori algorithm in restaurant management Solution

13. II.Capstone Project Introduction Felica: contactless smart card system Apriori algorithm: a classic algorithm for learning association rules. VIP customer management with card Data mining

14. III.Project Plan DEVELOPMENT MODEL

15. Development Model Reasons  Time constraint  On time, accepted quality  Specific phases  Equal weight: coding, testing

16. III.Project Plan

17. Team Working Online Offline

18. IV.Software Design Descriptions

19. Architecture Design

20. Component design

21. Entity Diagram

22. Felica Card

23. Outline I. Device II. Communication Protocol III. File System IV. Commands I. Device II. Communication Protocol III. File System IV. Commands

24. Device Reader / WriterFelica Card Pasori (RC – S600)RC – S885

25. Communication 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.

26. Communication Protocol Application layer Command Package Response Package Application layer Command Package Response Package

27. File System

28. Commands

29. Polling command  Use this command to acquire and identify a card Polling command  Use this command to acquire and identify a card Request Respons e

30. Commands Request Service  Use this command to check for the existence of Area / Service specified by Area Code / Service Code Request Response  Use this command to check whether a card exists Read Without Encryption  Use this command to read Block Data from a Service that requires no authentication. Request Service  Use this command to check for the existence of Area / Service specified by Area Code / Service Code Request Response  Use this command to check whether a card exists Read Without Encryption  Use this command to read Block Data from a Service that requires no authentication.

31. Commands Write Without Encryption  Use this command to write Block Data to a Service that requires no authentication. Request System Code  Use this command to acquire System Code of the System located on a card.  If a card is divided into multiple System, this command acquires System Code of all the System existing in the card. Authentication 1  Use this command to authenticate cards Write Without Encryption  Use this command to write Block Data to a Service that requires no authentication. Request System Code  Use this command to acquire System Code of the System located on a card.  If a card is divided into multiple System, this command acquires System Code of all the System existing in the card. Authentication 1  Use this command to authenticate cards

32. Commands Authentication 2  Use this command to allow a card to authenticate a Reader/Writer. Read  Use this command to read Block Data from a Service that requires authentication. Write  Use this command to write Block Data to a Service that requires authentication. Authentication 2  Use this command to allow a card to authenticate a Reader/Writer. Read  Use this command to read Block Data from a Service that requires authentication. Write  Use this command to write Block Data to a Service that requires authentication.

33. Apriori Algorithms

34. Outline 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)

35. Association Rules Overview  Association Rule : relations between variables in large databases. Eg (Bread, Butter) => (Milk)  Algorithms for finding association rules  Apriori algorithm :  Eclat algorithm  FP-growth algorithm  One-attribute-rule  Zero-attribute-rule  Association Rule : relations between variables in large databases. Eg (Bread, Butter) => (Milk)  Algorithms for finding association rules  Apriori algorithm :  Eclat algorithm  FP-growth algorithm  One-attribute-rule  Zero-attribute-rule

36. Apriori Overview  Best-known algorithm to mine association rules  Advantages  Find all rules  Simple  Disadvantages  Suffers from a number of inefficiencies or trade- offs  Operate in binary data only  Best-known algorithm to mine association rules  Advantages  Find all rules  Simple  Disadvantages  Suffers from a number of inefficiencies or trade- offs  Operate in binary data only

37. 3. Apriori Algorithms  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.  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.

38. 3. Apriori Algorithms  Generate strong association rules from frequent itemsets  Rules which satisfy the min support and min confidence.  Generate strong association rules from frequent itemsets  Rules which satisfy the min support and min confidence.

39. 3. Apriori Algorithms

40. Apriori Algorithms

41. Apriori Algorithms : Step1 Transaction ACD BCE ABCE BE L1-ItemsetSupport {A}2 {B}3 {C}3 {E}3 Min Support = 50 % Min Confidence = 80% L2-ItemsetSupport {AC}2 {BC}2 {BE}3 {CE}2 Joint Check Support ItemSupport {A}2 {B}3 {C}3 {D}1 {E}3 Check Support ItemSupport {AB}1 {AC}2 {AE}1 {BC}2 {BE}3 {CE}2

42. 3.1 Apriori Algorithms : Step1 Joint Check Support L2 L3 All subset of frequent Items must be frequent {ABCDEF} must combine with itemsets like {ABCDEG} ItemSupport {BCE}2 L2-ItemsetSupport {AC}2 {BC}2 {BE}3 {CE}2 L3-ItemsetSupport {BCE}2

43. Apriori Algorithms : Step1 Frequent ItemsSupport {A}2 {B}3 {C}3 {E}3 {AC}2 {BC}2 {BE}3 {CE}2 {BCE}2

44. Apriori Algorithms : Step2

45. 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)

46. Segmental Values Major disadvantage of Apriori Algorithms is that it must work on binary database. -> Must convert conventional database to binary database Value Types Category values Continuous values (eg. Age, money, ….) Major disadvantage of Apriori Algorithms is that it must work on binary database. -> Must convert conventional database to binary database Value Types Category values Continuous values (eg. Age, money, ….)

47. Segmental Values Fuzzy Set Triangle Function Fuzzy Set Triangle Function 0 1 a b c

48. Segmental Values Fuzzy Set Trapezoid Function Fuzzy Set Trapezoid Function 0 1 ab cd

49. 4.1. Segmental Values Age values (0->100) Young = F1(x,0,0,20,25) (red line) Middle = F2(x,20,30,40,45) (blue line) Old = F3(x,40,45,100,100) (yellow line) MinWT = 0.4 Age values (0->100) Young = F1(x,0,0,20,25) (red line) Middle = F2(x,20,30,40,45) (blue line) Old = F3(x,40,45,100,100) (yellow line) MinWT = 0.4 Example : if F1(43) = 0; F2(43) = 0.5; F3(43) = 0.6) => 43 year old person is consider as both Middle and Old

50. 4.2. Get Support This procedure is the most time consuming part in the algorithms. L1-ItemsetSupport {A}2 {B}3 {C}3 {E}3 L2-ItemsetSupport {AC}2 {BC}2 {BE}3 {CE}2 ItemSupport {AB}1 {AC}2 {AE}1 {BC}2 {BE}3 {CE}2 Joint Check Support ItemSupport {A}2 {B}3 {C}3 {D}1 {E}3 Check Support L1 L2

51. 4.2. Get Support Transaction ACDE BCE ABCE BCE AB SETElements 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 set (HASH SET)

52. 4.3. Weight Rules Rules are in form: A => B Eg: (Buying time = Morning & Buying Method = Online => Bill Amount = High) Some component are more interested than others (such as Bill Amount) => Each component is weighted Importance of rule A=>B is Rules are in form: A => B Eg: (Buying time = Morning & Buying Method = Online => Bill Amount = High) Some component are more interested than others (such as Bill Amount) => Each component is weighted Importance of rule A=>B is

53. VI. Testing  bases  The system including 5 components as: Authentication management Manager Cashier Accountant Apriori algorithm  The stages of testing: Unit test Integration test System test User Acceptance test final SRS specification detail design.

54. Test Plan NoFunctionalityTesterStatusPlanned Start DatePlanned End DateActual Start DateActual End Date 1Create test planHoaiNTcompleted3/1/20113/2/20113/1/20113/2/2011 Update test planHoaiNTcompleted3/2/2011 Review test planNhuNHBcompleted3/2/2011 2Create test cases completed3/2/20113/18/20113/2/20113/19/2011 2.1Create test cases for Authentication functionsNhuNHBcompleted3/2/20113/5/20113/2/20113/5/2011 Review test cases completed3/6/2011 Update test cases completed3/7/2011 2.2Create test cases for Manager ActorHoaiNTcompleted3/4/20113/8/20113/4/20113/8/2011 Review test cases completed3/9/2011 Update test cases completed3/10/2011 2.3Create test cases for Cashier ActorNhuNHBcompleted3/8/20113/11/20113/8/20113/11/2011 Review test cases completed3/12/2011 Update test cases completed3/13/20113/14/20113/13/20113/14/2011 2.4Create test cases for Accountant ActorNhuNHBcompleted3/15/20113/16/20113/15/20113/16/2011 Review test cases completed3/17/2011 Update test cases completed3/18/2011 2.5Create test cases for Apriori AlgorithmHoaiNTcompleted3/11/20113/14/20113/13/20113/17/2011 Review test cases completed3/15/20113/16/20113/18/2011 Update test cases completed3/17/20113/18/20113/19/2011 3Execute testNhuNHB,HoaiNTcompleted3/21/20114/1/20113/21/20114/10/2011

55. Test execution 1. Features to be tested Authentication Manager Cashier Accountant Apriori algorithm 2. Features not to tested None

56. Test report Sum of module codePassFailUntestedN/A Number of test cases 1071211295 Test coverage 98.92 % Test successful coverage 76.34 %

57. Defect Log Refer to CPro_IS_FeApRes_DefectLog.xls

58. communication working in group Felica technology Apriori algorithm project management

59. THANK YOU FOR YOUR ATTENTION