Conclusion - 以模糊邏輯建構的品質導向網際網路服務選擇模型

The study from QCMA to FMG-QCMA focused on the QoS-based web services selection. It performed QoS consensus from unique group structure to multi-groups framework for diversified web service consumers and to alleviate the differences on QoS characteristics in the complicated web services selection.

Regarding the proposed QCMA for unique group structure, it possesses the following features.

1. QCMA is a web service selection mechanism based on fuzzy QoS consensus for a group of participants. The architecture allows them to reach QoS consensus by including a number of activities such as participants’ opinion similarity, QoS term preference ordering and QoS fuzzy scale for each QoS term. The contribution of QCMA not only includes the fuzzy inquiry for service selection, but also offers the features to model the QoS preference consensus after aggregating sufficient _a^k

wsa . i

2. QCMA is designed for open and dynamic web environment, such that new opinions and preferences as well as new QoS aspects can be modeled flexibly.

Regarding the proposed FMG-QCMA, elaborating higher precision and efficient QoS-aware selection of web service than unique-group-based scheme (QCMA) and some advantage on marketing web service, FMG-QCMA can further possess the following conclusion.

1. FMG-QCMA is a web service selection mechanism based on fuzzy QoS consensus for multi-groups of participants. The architecture allows them to be fuzzily clustered into appropriate sub-groups to reach QoS consensus by including a number of activities such as participants’ opinion similarity and QoS fuzzy scale for each QoS attribute.

2. The similarity analysis for multi-attributes-based QoS defined by W3C [4] can be performed via multi-attributes-based clustering by FMGSAM in FMG-QCMA. The different weight over QoS attributes and similarity for each individual QoS attribute are thought over, too.

3. The FMGSAM achieve higher similarity with multi-groups opinions clustering due to reasoning multi-attributes QoS from different background. The improvement in similarity by FMGSAM than SAM has been proven in experiment.

4. The FMGSAM also achieve higher efficiency under multi-groups framework. The improvement in efficiency can be formulated as (1 – (O(n₁²) + O(n₂²) …+ O(n_m²)) / O(n²)), n = n1 + n2 + …. + nm .

5. The QoS feedback from web service consumers that closes to “group boundary in similarity” will be clustered as “fuzzily similar” by fuzzy comparison. These QoS that should be also significant in similarity will be thought over so that the consensus based on the similarity analysis will be more credible than hard clustering scheme.

6. With the multi-groups-based framework established by FMGSAM, different preference order generated by RMGDP among different clustered opinion sub-groups based on higher similarity is allowed and generated in higher practicability.

7. The similarity threshold

d~ can be effectively / efficiently moderated by feedback for delivered fuzzy QoS opinions issued by web service consumers. It makes FMG-QCMA being capable of deciding an appropriate

d~ to cluster all collected fuzzy QoS opinions according to real perception from web service consumers.

FMG-QCMA also reports its improvements on QCMA in terms of similarity measurement and system efficiency. The FMGSAM achieve higher similarity, as it adopts an

effective multi-groups opinions clustering according to service consumers’ QoS disposition. It also achieves higher efficiency, as its improvement in efficiency is evident shown in Table 14.

In the dynamic world, customers’ perception could be not always kept on fixed level and would be moderated by his / her changeable mind due to growth from learning more experience. In the study of FMG-QCMA we have thought over this factor but still can be further discussed. This dynamic phenomenon could impact the factors to re-cluster fuzzy QoS opinions such as similarity threshold

d~ , weight wi and corresponded β in CDC generation, etc.. The representation of fuzzy QoS opinions could be also revised to fit in more elaborated customers’ perception. These conditions mentioned above would be significant in future work for the series of research in web service selection.

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Appendix A: Raw Data for QCMA Validation

A.1: The 50 Fuzzy QoS Opinions for QCMA (1/2): QoS Attributes a1 ~ a7

aij: x_jof , according to equation (1) and (2).

a11 a12 a13 a14 a21 a22 a23 a24 a31 a32 a33 a34 a41 a42 a43 a44 a51 a52 a53 a54 a61 a62 a63 a64 a71 a72 a73 a74

Consumer001 0 0 7 8 0 0 5 9 0 0 4 7 0 0 7 8 0 0 4 8 0 0 5 8 0 0 7 8

Consumer002 0 0 6 8 0 0 6 9 0 0 5 9 0 0 7 9 0 0 3 5 0 0 4 7 0 0 5 9

Consumer003 0 0 8 9 0 0 6 10 0 0 3 8 0 0 5 7 0 0 6 9 0 0 4 8 0 0 7 9

Consumer004 0 0 3 8 0 0 4 7 0 0 6 8 0 0 7 8 0 0 5 7 0 0 6 9 0 0 7 9

Consumer005 0 0 7 9 0 0 6 8 0 0 5 8 0 0 6 8 0 0 5 8 0 0 5 8 0 0 4 8

Consumer006 0 0 5 9 0 0 6 9 0 0 6 9 0 0 7 10 0 0 6 8 0 0 6 9 0 0 6 7

Consumer007 0 0 4 7 0 0 7 9 0 0 7 10 0 0 5 9 0 0 8 10 0 0 4 7 0 0 5 9

Consumer008 0 0 5 7 0 0 5 8 0 0 5 8 0 0 6 9 0 0 8 9 0 0 5 8 0 0 7 8

Consumer009 0 0 5 8 0 0 4 8 0 0 6 8 0 0 7 9 0 0 7 9 0 0 6 9 0 0 6 7

Consumer010 0 0 7 9 0 0 6 9 0 0 4 8 0 0 4 8 0 0 5 7 0 0 5 8 0 0 5 8

Consumer011 0 0 5 7 0 0 5 8 0 0 7 9 0 0 6 9 0 0 7 9 0 0 7 9 0 0 6 9

Consumer012 0 0 6 9 0 0 6 9 0 0 6 9 0 0 5 9 0 0 6 8 0 0 6 8 0 0 5 6

Consumer013 0 0 3 7 0 0 7 10 0 0 5 8 0 0 7 8 0 0 6 9 0 0 5 8 0 0 5 8

Consumer014 0 0 5 8 0 0 3 8 0 0 7 9 0 0 6 9 0 0 5 8 0 0 4 9 0 0 6 8

Consumer015 0 0 6 8 0 0 5 8 0 0 6 7 0 0 7 9 0 0 7 9 0 0 6 7 0 0 7 8

Consumer016 0 0 7 10 0 0 6 9 0 0 6 8 0 0 7 9 0 0 4 7 0 0 5 8 0 0 6 9

Consumer017 0 0 4 7 0 0 4 8 0 0 7 9 0 0 6 9 0 0 6 8 0 0 7 9 0 0 5 8

Consumer018 0 0 8 9 0 0 7 9 0 0 5 7 0 0 5 7 0 0 7 9 0 0 6 8 0 0 6 9

Consumer019 0 0 7 9 0 0 6 9 0 0 6 8 0 0 7 9 0 0 8 10 0 0 6 9 0 0 7 8

Consumer020 0 0 5 8 0 0 5 8 0 0 6 8 0 0 8 10 0 0 5 8 0 0 5 8 0 0 6 9

Consumer021 0 0 6 9 0 0 6 8 0 0 7 8 0 0 4 7 0 0 6 8 0 0 6 7 0 0 6 7

Consumer022 0 0 4 8 0 0 7 9 0 0 5 8 0 0 7 9 0 0 4 9 0 0 6 8 0 0 5 8

Consumer023 0 0 7 8 0 0 6 8 0 0 6 7 0 0 6 7 0 0 7 8 0 0 5 8 0 0 5 8

Consumer024 0 0 5 8 0 0 5 9 0 0 4 6 0 0 5 8 0 0 4 9 0 0 5 9 0 0 5 9

Consumer025 0 0 6 8 0 0 7 8 0 0 4 7 0 0 7 9 0 0 6 7 0 0 6 8 0 0 7 8

Consumer026 0 0 6 9 0 0 6 9 0 0 7 10 0 0 7 8 0 0 5 8 0 0 6 7 0 0 6 8

Consumer027 0 0 6 7 0 0 4 7 0 0 7 8 0 0 6 8 0 0 3 8 0 0 4 9 0 0 7 9

Consumer028 0 0 5 9 0 0 7 9 0 0 5 8 0 0 5 8 0 0 7 9 0 0 7 8 0 0 6 9

Consumer029 0 0 4 8 0 0 8 10 0 0 6 7 0 0 8 9 0 0 7 9 0 0 6 10 0 0 7 8

Consumer030 0 0 6 9 0 0 5 8 0 0 3 7 0 0 7 9 0 0 5 7 0 0 5 7 0 0 5 9

Consumer031 0 0 7 9 0 0 6 9 0 0 7 9 0 0 8 10 0 0 6 8 0 0 6 9 0 0 6 8

Consumer032 0 0 5 8 0 0 4 7 0 0 6 9 0 0 7 8 0 0 4 8 0 0 5 6 0 0 7 9

Consumer033 0 0 8 10 0 0 5 8 0 0 5 8 0 0 5 9 0 0 7 9 0 0 4 7 0 0 6 9

Consumer034 0 0 5 9 0 0 6 9 0 0 7 9 0 0 7 10 0 0 6 8 0 0 7 9 0 0 7 8

Consumer035 0 0 7 8 0 0 7 8 0 0 6 8 0 0 7 9 0 0 5 8 0 0 6 8 0 0 8 9

Consumer036 0 0 6 9 0 0 5 7 0 0 5 7 0 0 5 7 0 0 4 7 0 0 5 7 0 0 6 7

Consumer037 0 0 7 9 0 0 7 9 0 0 6 8 0 0 6 9 0 0 6 8 0 0 7 8 0 0 4 9

Consumer038 0 0 5 8 0 0 8 9 0 0 4 8 0 0 4 7 0 0 5 9 0 0 6 8 0 0 5 8

Consumer039 0 0 7 9 0 0 4 8 0 0 6 9 0 0 7 8 0 0 7 8 0 0 5 9 0 0 6 8

Consumer040 0 0 4 7 0 0 5 7 0 0 7 8 0 0 5 7 0 0 5 7 0 0 6 8 0 0 5 7

Consumer041 0 0 6 8 0 0 6 9 0 0 5 8 0 0 6 9 0 0 6 8 0 0 7 9 0 0 5 6

Consumer042 0 0 5 8 0 0 5 7 0 0 6 7 0 0 7 8 0 0 6 9 0 0 6 7 0 0 4 8

Consumer043 0 0 7 9 0 0 6 8 0 0 7 8 0 0 5 7 0 0 5 8 0 0 6 8 0 0 6 9

Consumer044 0 0 6 9 0 0 7 8 0 0 6 9 0 0 6 8 0 0 7 8 0 0 7 9 0 0 6 8

Consumer045 0 0 6 10 0 0 5 8 0 0 7 8 0 0 7 8 0 0 4 6 0 0 5 7 0 0 5 7

Consumer046 0 0 7 7 0 0 6 6 0 0 5 7 0 0 5 6 0 0 7 9 0 0 6 7 0 0 7 8

Consumer047 0 0 5 6 0 0 8 8 0 0 6 8 0 0 6 8 0 0 6 9 0 0 5 6 0 0 5 8

Consumer048 0 0 4 8 0 0 7 9 0 0 7 8 0 0 7 8 0 0 5 8 0 0 7 8 0 0 6 7

Consumer049 0 0 7 9 0 0 5 8 0 0 6 8 0 0 6 9 0 0 7 8 0 0 4 9 0 0 5 9

Consumer050 0 0 8 9 0 0 6 9 0 0 7 9 0 0 5 8 0 0 6 9 0 0 6 8 0 0 6 8

A.2: The 50 Fuzzy QoS Opinions for QCMA (2/2): QoS Attributes a8 ~ a13

aij: x_jof , according to equation (1) and (2).

a81 a82 a83 a84 a91 a92 a93 a94 aa1 aa2 aa3 aa4 ab1 ab2 ab3 ab4 ac1 ac2 ac3 ac4 ad1 ad2 ad3 ad4

Consumer001 0 0 3 8 0 0 6 9 0 0 9 10 0 0 6 7 0 0 4 8 0 0 6 9

Consumer002 0 0 6 8 0 0 5 7 0 0 6 9 0 0 7 10 0 0 7 9 0 0 4 7

Consumer003 0 0 5 8 0 0 5 7 0 0 6 9 0 0 3 7 0 0 5 7 0 0 6 9

Consumer004 0 0 4 7 0 0 7 10 0 0 5 7 0 0 3 8 0 0 6 9 0 0 4 7

Consumer005 0 0 7 9 0 0 6 8 0 0 6 8 0 0 6 8 0 0 5 6 0 0 6 8

Consumer006 0 0 6 8 0 0 5 7 0 0 6 9 0 0 6 7 0 0 6 8 0 0 4 9

Consumer007 0 0 7 9 0 0 4 8 0 0 5 7 0 0 5 8 0 0 4 7 0 0 5 7

Consumer008 0 0 5 7 0 0 6 8 0 0 6 9 0 0 4 8 0 0 5 6 0 0 6 8

Consumer009 0 0 6 8 0 0 5 7 0 0 5 7 0 0 5 6 0 0 5 7 0 0 4 7

Consumer010 0 0 8 10 0 0 6 9 0 0 5 7 0 0 4 8 0 0 4 8 0 0 5 8

Consumer011 0 0 6 8 0 0 5 8 0 0 3 8 0 0 7 9 0 0 5 7 0 0 3 8

Consumer012 0 0 7 7 0 0 5 7 0 0 6 7 0 0 6 8 0 0 3 6 0 0 5 8

Consumer013 0 0 5 8 0 0 6 8 0 0 5 8 0 0 5 7 0 0 6 7 0 0 6 7

Consumer014 0 0 6 9 0 0 4 8 0 0 6 8 0 0 4 7 0 0 5 8 0 0 4 7

Consumer015 0 0 4 7 0 0 5 8 0 0 7 9 0 0 5 8 0 0 4 7 0 0 5 7

Consumer016 0 0 4 7 0 0 6 7 0 0 5 8 0 0 7 9 0 0 3 6 0 0 5 8

Consumer017 0 0 5 7 0 0 5 8 0 0 6 8 0 0 5 6 0 0 4 7 0 0 5 6

Consumer018 0 0 3 8 0 0 7 9 0 0 6 7 0 0 6 7 0 0 4 8 0 0 6 7

Consumer019 0 0 7 8 0 0 6 8 0 0 7 8 0 0 5 9 0 0 5 7 0 0 5 8

Consumer020 0 0 6 7 0 0 5 8 0 0 7 8 0 0 5 8 0 0 4 6 0 0 4 5

Consumer021 0 0 7 9 0 0 4 7 0 0 8 10 0 0 6 7 0 0 6 7 0 0 5 6

Consumer022 0 0 6 8 0 0 5 8 0 0 6 8 0 0 4 6 0 0 4 8 0 0 4 8

Consumer023 0 0 5 7 0 0 6 6 0 0 5 9 0 0 7 9 0 0 5 7 0 0 6 6

Consumer024 0 0 4 8 0 0 3 8 0 0 7 8 0 0 5 8 0 0 3 6 0 0 5 8

Consumer025 0 0 6 9 0 0 6 8 0 0 4 7 0 0 4 9 0 0 4 7 0 0 4 5

Consumer026 0 0 5 7 0 0 5 8 0 0 6 8 0 0 8 10 0 0 5 8 0 0 5 7

Consumer027 0 0 7 8 0 0 4 7 0 0 5 7 0 0 5 9 0 0 4 6 0 0 4 7

Consumer028 0 0 6 9 0 0 5 7 0 0 6 9 0 0 6 7 0 0 6 7 0 0 3 6

Consumer029 0 0 5 8 0 0 6 8 0 0 7 8 0 0 6 7 0 0 5 7 0 0 5 8

Consumer030 0 0 6 7 0 0 4 8 0 0 5 6 0 0 5 8 0 0 5 6 0 0 6 7

Consumer031 0 0 4 8 0 0 5 7 0 0 6 7 0 0 4 7 0 0 4 7 0 0 5 8

Consumer032 0 0 6 9 0 0 6 8 0 0 7 8 0 0 5 8 0 0 3 8 0 0 4 6

Consumer033 0 0 5 7 0 0 4 7 0 0 5 8 0 0 6 7 0 0 4 6 0 0 5 7

Consumer034 0 0 4 6 0 0 5 7 0 0 7 9 0 0 6 9 0 0 5 7 0 0 6 8

Consumer035 0 0 7 8 0 0 4 8 0 0 5 8 0 0 4 6 0 0 5 8 0 0 5 7

Consumer036 0 0 6 8 0 0 5 8 0 0 6 8 0 0 5 7 0 0 6 7 0 0 4 8

Consumer037 0 0 6 9 0 0 6 7 0 0 7 8 0 0 5 9 0 0 6 9 0 0 5 7

Consumer038 0 0 6 8 0 0 4 7 0 0 6 7 0 0 7 8 0 0 4 6 0 0 6 8

Consumer039 0 0 5 9 0 0 7 8 0 0 6 7 0 0 6 7 0 0 5 7 0 0 5 6

Consumer040 0 0 6 8 0 0 5 7 0 0 4 8 0 0 5 8 0 0 6 8 0 0 4 9

Consumer041 0 0 5 9 0 0 6 8 0 0 7 8 0 0 6 9 0 0 5 6 0 0 6 8

Consumer042 0 0 4 7 0 0 5 8 0 0 6 7 0 0 5 7 0 0 4 7 0 0 5 8

Consumer043 0 0 6 8 0 0 5 8 0 0 5 6 0 0 6 8 0 0 5 8 0 0 5 7

Consumer044 0 0 5 9 0 0 6 8 0 0 4 7 0 0 5 9 0 0 6 9 0 0 4 8

Consumer045 0 0 6 7 0 0 6 8 0 0 5 8 0 0 6 8 0 0 5 7 0 0 5 7

Consumer046 0 0 7 8 0 0 7 7 0 0 6 7 0 0 5 8 0 0 4 8 0 0 6 8

Consumer047 0 0 5 7 0 0 6 8 0 0 6 7 0 0 6 9 0 0 5 8 0 0 5 5

Consumer048 0 0 6 6 0 0 5 6 0 0 4 7 0 0 6 9 0 0 6 9 0 0 4 6

Consumer049 0 0 5 7 0 0 6 7 0 0 3 7 0 0 6 8 0 0 4 7 0 0 5 7

Consumer050 0 0 7 8 0 0 5 8 0 0 7 8 0 0 5 9 0 0 5 9 0 0 6 8

A.3: The Order Preference for 50 Participants in QCMA

Appendix B: Algorithm Fuzzy Clustering

Algorithm Fuzzy_Clustering(

WSA )

/* The algorithm assumes the definitions: K, the set of consumers; _S^k

wsa Q , the set of trapezoidal opinions for consumer, k, over the set of attributes, SQ; WSA_S_Q is the collection of all the _S^k

wsa Q ; and G_p is a subset of K containing the consumers in cluster, p.

1. WSA _temp_S_Q WSA_S_Q ; /* Copy all incoming opinions into a temporary set for clustering.

2. p  0; /* p is set as subgroup ID and initialized as 0

3. while WSA _temp_S_Q is not empty /* Clustering Loop for a created group.

4. j  min

Q S

S WSA temp

wsa K k

k | , _

{   and

} ,

. _p _p

p k

S G Abs Sim G all created G

wsa Q  _ _ .

5. p  p + 1; /* Set Subgroup ID.

6. max_p  p; /* Record the maximum group index in the clustering.

7. ^p

wsaS  _S^j

wsa Q; /* Set group centre for G_p with the minimum index of opinion from step 4.

8. WSA _tempSQWSA _temp_S_Q - { _S^j

wsa Q}; /* Remove the opinion _S^j

wsa Q from evaluated list.

9. cluster _temp_S_QWSA _temp_S_Q; /* Copy the temporary set to the other set for comparison in clustering.

10. G_p.Abs_Sim  { ^p

wsaS }; /* Insert group centre to “Similar Area” in G_p. 11. n_t^G^p  1; /* Initialize n_t^G^p: no. of _S^j

wsa Qin G_p.

12. while cluster _temp_S_Q is not empty /* Cluster all evaluated opinions in set for

comparison. opinion due to step 17.

20. else

21. G_p.Fuz_Sim  G_p.Fuz_Sim + { _S^j

wsa Q}; /* Insert evaluated opinion into

“like Similar Area” but the evaluated opinion will be kept for next round.

22. endif /* if ( _S^G^p^j evaluation for comparison.

25. end while cluster _temp_S_Q is not empty /* Go evaluation for next opinion.

26. end while WSA _temp_S_Q is not empty /* Go to next clustered group.

27. end Algorithm Fuzzy_Clustering(WSA_S_Q);

Appendix C: Algorithm SimVerifier

Algorithm SimVerifier( _S^jk

Sim Q , sim_operator,

d~ )

/* sim_result: an indicator for the similarity verification by comparison between _S^jk Sim Q and

d~ .

1. sim_result  0; /* Initialize sim_result as 0.

/ * Do similarity comparison over 13 QoS attributes and convert to sim_result for further

/* Aug is a variable to augment a value to become distinguishable. In this case 3 is sufficient.

6. Aug = 3; sim_result  Aug (sim_result / 13);

7. Case sim_operator of 8. “~”: /* ( _S^jk

Sim Q ~

) is recognized.

9. if ( _c^l _S

) is recognized.

11. if ( _c^u_S

) is recognized.

13. if ( _c^l _S

) is recognized.

15. if (0 ≦ sim_result < _c^u _S

) is recognized.

17. if (0 ≦ sim_result < _c^l _S

f _{_} Q) then return (sim_result) else return (-1);

18. end Case; /* sim_operator 19. End Algo. SimVerifier( _S^jk

Sim Q , sim_operator,

d~ ));

Appendix D: Algorithm Clustering Verification

Algorithm Clustering_Verification( _S^j

wsa Q, s_feedback, group_ID) /* Identify if _S^j

wsa Q was on “Similar Area” or “Like Similar Area”, from the first group it was allocated.

1. p_Sim_Type  GetSimType( _S^j

wsa Q, group_ID); /* Return if _S^j

wsa Q is E_Fail_CDC, E_Fuz_Sim or E_Abs_Sim.

2. if Validation(group_ID) is true then /* group_ID is valid.

/* Verify the cases of s_feedback: Fail CDC (detecting by CDC threshold) or later mismatched feedback.

3. Case s_feedback of

/* Verify the conditions if the CDC for _S^j

wsa Qis less than the CDC threshold of evaluated clustered group.

4. E_Fail_CDC:

5. m_count_fdistance_too_long  m_count_fdistance_too_long + 1;

6. if m_count_fdistance_too_long ≧m_threshold_distortion then 7. if _S^l

d Q≧ 0.02 /* Moderate _S^l d Q . 8. _S^l

d Q  _S^l

d Q – 0.02;

9. _S^u d Q _S^u

d Q – 0.02;

10. Fuzzy_Clustering(WSA_S_Q );

11. endif /* _S^l

d Q ≧ 0.02.

12. endif /* if m_count_fdistance_too_long ≧m_threshold_distortion.

/* Verify the conditions if _S^j

wsa Qwas allocated into mismatched area..

13. Otherwise:

14. Case p_Sim_Type of 15. E_Fuz_Sim:

16. if s_feedback = E_Not_Sim then

m_count_fdistance_too_longm_count_fdistance_too_long + 1;

17. if s_feedback = E_Abs_Sim then

m_count_fdistance_too_short m_count_fdistance_too_short + 1;

18. E_Abs_Sim:

19. if (s_feedback = E_Not_Sim) or (s_feedback = E_Fuz_Sim) then 20. m_count_fdistance_too_long  m_count_fdistance_too_long + 1;

21. endif /* if (s_feedback = E_Not_Sim) or (s_feedback = E_Fuz_Sim) 22. Otherwise: /* Allocate this _S^j

wsa Q into appropriate group.

23. for p = 1 to max_p 24. if SimVerify( _S^G^p^j

Sim Q , “~”,

) > 0 then /* _S^G^p^j Sim Q ~

d~ . 25. n_t^G^p  n_t^G^p + 1;

26. if SimVerify( _S^G^p^j

Sim Q , “~”,

) > 0 then /* _S^G^p^j Sim Q ~

d~ ,

j S_Q

wsa should be clustered.

27. G_p.Abs_Sim  G_p.Abs_Sim + { _S^j

wsa Q}; /* Insert opinion into

“Similar Area” in G_p. 28.

temp

WSA _ 

temp

WSA _ - { _S^j

wsa Q }; /* Remove the opinion due to step 17.

29. break; /* Terminate Algorithm Clustering_Verification when just allocate _S^j

wsa Q. 30. else

/* Insert opinion into “like Similar Area” but the evaluated opinion will be kept for next round.

31. G_p.Fuz_Sim  G_p.Fuz_Sim + { _S^j wsa Q };

32. endif /* if ( _S^G^p^j Sim Q ~

d~ ) 33. endif /* if ( _S^G^p^j

Sim Q ~

d~ ) 34. end for p = 1 to max_p

35. end Case; /* p_Sim_Type

/* Determine if re-clustering by moderated threshold for similarity should be

enabled or not.

36. if m_count_fdistance_too_long ≧m_threshold_distortion then 37. if _S^l

d Q≧ 0.02 /* Moderate _S^l d Q . 38. _S^l

d Q  _S^l

d Q – 0.02;

39. _S^u d Q _S^u

d Q – 0.02;

40. Fuzzy_Clustering(

WSA );

41. endif /* _S^l

d Q ≧ 0.02

42. endif /* if m_count_fdistance_too_long ≧m_threshold_distortion.

43. if m_count_fdistance_too_short ≧m_threshold_distortion then 44. if _S^u

d Q≦ 0.98 /* Moderate _S^u d Q . 45. _S^u

d Q _S^u

d Q + 0.02;

46. _S^l d Q  _S^l

d Q + 0.02;

47. Fuzzy_Clustering(WSA_S_Q );

48. endif /* _S^u

d Q≦ 0.98.

49. endif /* if m_count_fdistance_too_short ≧m_threshold_distortion.

50. end Case; /* s_feedback.

51. endif /* if Validation(group_ID) is true.

52. end Algorithm Clustering_Verification( _S^j

wsa Q, s_feedback, group_ID);

個人簡歷

博士論文作者: 林謂立

1968 年出生於台灣省宜蘭縣。

1986 年畢業於宜蘭高級中學。

1990 年畢業於私立大同工學院資訊工程學系，獲得工程學士學位。

2003 年畢業於國立交通大學管理學院在職專班資管組，獲得管理學碩士學位。

2003 年~2009 年進入國立交通大學資訊管理研究所博士班，進修資訊管理學博士學位。

在進修博士學位期間，曾發表六篇論文，其中包含兩篇期刊論文於 A 級 SCI 期刊(JCSS, Journal of Computer and System Sciences)及四篇學術會議論文(其中錄取於 AINA2006 之學術會議論文集為 EI 學術會議論文)。發表之論文列舉如下:

Journal Paper (期刊論文):

1. Wei-Li Lin, Chi-Chun Lo, Kuo-Ming Chao, Muhammad Younas, Consumer-centric QoS-aware selection of web services, Journal of Computer and System Sciences, pp 211-231, 2008 (SCI, 2007 Impact Factor: 1.185, accepted on 31 Oct 2006, available online on 24 April 2007)

2. Wei-Li Lin, Chi-Chun Lo, Kuo-Ming Chao, Nick Godwin, Web Services for Multi-Group QoS Consensus, Journal of Computer and System Sciences, (SCI, 2007 Impact Factor: 1.185, accepted on 16 June 2009)

Conference Paper (會議論文):

1. Wei-Li Lin, Chi-Chun Lo, Kuo-Ming Chao, Nick Godwin, Fuzzy Similarity Clustering for Intelligent Consumer-centric QoS-aware Selection of Web Services, The Second International Workshop on Adaptive Systems in Heterogeneous Environments (ASHEs 2009), Fukuoka, Japan, 16 Mar 2009.

2. Wei-Li Lin, Chi-Chun Lo, Kuo-Ming Chao, Muhammad Younas, Fuzzy Consensus on QoS in Web Services Discovery, IEEE AINA 2006 conference, Vienna, Austria, Proceeding Vol.1 791-798, 18 Apr 2006 (EI).

3. Wei-Li Lin, Chi-Chun Lo, Jay Wu, An XCS-Based Intelligent Searching Model for cross-organization identity management in Web service, The Fourth International Conference on Electronic Business (ICEB2004), Beijing, China, 05 Dec 2004.

4. An-Ping Chen, Wei-Li Lin, Yen-Chu Chen, An Intelligent Model for Stock Investment with Buffett Strategy、Classifier System、Neural Network and Linear Programming, The Fourth International Conference on Electronic Business (ICEB2004), Beijing, China, 05 Dec 2004.

在產業界經歷方面:

1992 年~1998 年任職法商 Alcatel-TAISEL 通訊系統研發工程師。

1998 年~1999 年任職 ERP 軟體商崧揚科技創始股東暨業務專案經理。

1999 年~2003 年任職德商 Siemens Telecommunication (Taiwan)業務部門經理。

2004 年~2005 年兼任軟交換系統公司 Walkersun Technologies 總經理特別助理。

2005 年~2006 年同時任職亞太電信 M 化事業群業務處、行銷處、產品開發處協理。

2006 年~2009 年任職行動美商電信網管系統公司 Groundhog Technologies 副總經理暨 大中國區執行副總經理。

在文檔中以模糊邏輯建構的品質導向網際網路服務選擇模型 (頁 89-108)