1. Ranking services for composition Hong Qing Yu (Harry)

2. Service composition “Composition of Web services has received much interest to support business-to-business or enterprise application integration.” [1] Static Dynamic [2]

3. Issues for composition Global services registration Service search/discovery Understanding composition requirements Service selection Workflow generation Service invoking

4. Ranking problem for selection If there are more than two services satisfying functional requirements, Which one is best to use? Cheapest one Fastest one Best performance Other non-functional properties. Logic Scoring preference is a technique can help us.

5. Logic scoring preference Traditional Scoring Techniques are simple E=W 1 E 1 +W 2 E 2 +…+W n E n, 0 ≤ E ≤ 1. There is a problem [4] It is regardless of the level of importance, the contribution of component E i to the global score is limited to W i LSP (Logic Scoring preference)

6. Logic scoring preference Differences are r & W E=(W 1 E r 1 +W 2 E r 2 +…+W n E r n ) 1/r, 0 ≤ E ≤ 1, W 1 +W 2 +…+W n =1, W i >0, i=1,2,…,n. r is a real number selected to achieve the desired logical properties of the aggregation function

7. Logic scoring preference [4] [5]

8. Ranking by composition context : is an European project The meaning of context in the project Context affects service selection We need a simpler way to define r

9. Designing evaluation rules E=(W 1 E r 1 +W 2 E r 2 +…+W n E r n ) 1/r, 0 ≤ E ≤ 1, W 1 +W 2 +…+W n =1, W i >0, i=1,2,…,n. 1. Filtering rules 2. Evaluation function 3. r selection

10. Filtering rules Cost<$35Speed>30/s Quality>85 Irreplaceable preference criteria Replaceable preference criteria If the service’s properties do not achieve the irreplaceable preference, then it will be filtered out.

11. Evaluation function Exact match E s =1 (if the criteria is matched) or 0 (if is not matched) Set overlap E s =(e 1 +e 2 +…+e i ) /i (with E i being a score for each criteria) Level match if i is the number of levels and i c is current service level value, then we define: E s =i c /i

12. Evaluation function Specific value if v x is the maximum value of all relevant services in one criteria, v n is the minimum value and vi is the current service value, then we calculate:

13. r selection E=W 1 E 1 +W 2 E 2 +...W n E n Can we compute the weight for choosing the r instead of using the way introduced in [5]. On the one hand, Filter makes all aspects criteria is replaceable, which means that we need conjunction. On the other hand, if the weight of each criterion are so difference, we also need disjunction.

14. r selection rules We are in a very balanced position, and we can narrow our r selection tables To simplify defining the r value, we just select 1.5, 1, 0.5. If (highest weight – lowest weight)>average weight, then r=1.5 If (highest weight – lowest weight)<average weight, then r=0.5 If (highest weight – lowest weight)=average weight, then r=1

15. Example

16. Worked Example Criterion requirement: 1. More people’s weight=0.6 2. Quality’s weight=0.3 3. Cost’s weight=-0.1 The result E skype =(2/3) 1.5 ·0.6+(2/3) 1.5 ·0.3+1 1.5 ·0.1=0.590 E talkfly =1 1.5 ·0.6 +(1/3) 1.5 ·0.3+0=0.658 E hotmail =1 1.5 ·0.6+1 1.5 ·0.3+(0.6) 1.5 ·0.1=0.946

17. References 1. http://www.zurich.ibm.com/pdf/ebizz/icaps-ws.pdf http://www.zurich.ibm.com/pdf/ebizz/icaps-ws.pdf 2. http://www.active-endpoints.com/open-source- tutorial.htm http://www.active-endpoints.com/open-source- tutorial.htm 3. http://www.isi.edu/~thakkar/icaps2003-p4ws.pdf http://www.isi.edu/~thakkar/icaps2003-p4ws.pdf 4. http://citeseer.ist.psu.edu/cache/papers/cs/2874/http:zSzzS zcs.sfsu.eduzSzpeoplezSzjozozSzlsp.pdf/a-method-for- evaluation.pdf http://citeseer.ist.psu.edu/cache/papers/cs/2874/http:zSzzS zcs.sfsu.eduzSzpeoplezSzjozozSzlsp.pdf/a-method-for- evaluation.pdf 5. “Continuous Preference Logic for System Evaluation”, Jozo J. Dujmovic, USA

18. Thanks Questions