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inv:composit:validation [2013/02/28 18:17] – pablo.rodriguez.mierinv:composit:validation [2013/02/28 18:31] (actual) – pablo.rodriguez.mier
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-Abstract—One major advantage of web services is the ability to be combined to create composite services on-demand+//Abstract//—One major advantage of web services is the ability to be combined to create composite services on-demand
 through automatic composition techniques. However, although the inclusion of semantics allows a greater precision in the through automatic composition techniques. However, although the inclusion of semantics allows a greater precision in the
 description of their functionality, and therefore greater composition capabilities, the current application of Semantic Web Services description of their functionality, and therefore greater composition capabilities, the current application of Semantic Web Services
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 | WSC'08 08  | 8119  | 30          | 20      | 5.44 / 6.54   | 5          | 4         | | WSC'08 08  | 8119  | 30          | 20      | 5.44 / 6.54   | 5          | 4         |
  
- 
-These tables show: the number of services of each dataset (column #Serv); the number of services of the optimal solution (column #Serv. Sol.); the length of the shortest solution (column #Length); the average number of inputs and outputs (column  Avg. In./Out.); the number of inputs provided by the user (column #Init. con.) and the number of wanted output concepts (column #Goal con.). The goal is to find, for each dataset, the optimal 
-composite service (minimum number of services, minimum length) that satisfies the goal concepts, using only the initial inputs provided. 
  
 Exact-Matching datasets were calculated by extending the outputs of each web service, including all superclasses of each output as an output of the service itself (semantic expansion). Thus, the average number of outputs is bigger than in the other datasets. The semantic expansion transforms a semantic matching problem into a exact matching problem, when exact and plug-in match is used to perform the semantic matchmaking. This allows us to test composition algorithms (that do not use semantic reasoners) with the WSC'08 datasets. For example, suppose that a service S1 provides the instance "my_car" which is an instance of the concept "sedan", whereas another service S2 requires an input "car". Given that "my_car" is a sedan "car" (plug-in match), S2 can use it as an input. This behavior can be simulated by adding superclasses of "my_car" to the service. Thus, service S1 Exact-Matching datasets were calculated by extending the outputs of each web service, including all superclasses of each output as an output of the service itself (semantic expansion). Thus, the average number of outputs is bigger than in the other datasets. The semantic expansion transforms a semantic matching problem into a exact matching problem, when exact and plug-in match is used to perform the semantic matchmaking. This allows us to test composition algorithms (that do not use semantic reasoners) with the WSC'08 datasets. For example, suppose that a service S1 provides the instance "my_car" which is an instance of the concept "sedan", whereas another service S2 requires an input "car". Given that "my_car" is a sedan "car" (plug-in match), S2 can use it as an input. This behavior can be simulated by adding superclasses of "my_car" to the service. Thus, service S1
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 === 2. Semantic-Matching evaluation results === === 2. Semantic-Matching evaluation results ===
  
-In this experiment, we evaluate the performance of ComposIT and PORSCE-II with the semantic-matching datasets. Note that OWLS-Xplan does not support semantic matchmaking. Therefore, this algorithm has been excluded from this evaluation and also from the WSC'08 evaluation, which requires semantic matchmaking. +Results of the semantic analysis done to detect the optimal values of the thresholds for PORSCE-II.
- +
-PORSCE-II uses different threshold values to configure the maximum semantic concept distance allowed for a valid semantic matching. +
-The values of these thresholds are directly related to the performance of the algorithm so that the higher the thresholds, the slower the algorithm runs, but the better the quality of the solutions. Given the difficulty of selecting an appropriate value for the threshold without compromising too much the performance, we selected the optimal value for each dataset based on a study using the Semantic-matching datasets in which we analyzed the performance of PORSCE-II by using incremental values of the threshold from 1 to 19, which corresponds with the maximum depth for all ontologies.+
  
 ^  Plug-in threshold  ^  Time (for Semantic-Matching 01)  ^  % Datasets solved  ^ Semantic-Matching datasets solved  ^  Performance  ^ ^  Plug-in threshold  ^  Time (for Semantic-Matching 01)  ^  % Datasets solved  ^ Semantic-Matching datasets solved  ^  Performance  ^
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 | 19                  | 58981.02                          | 60%                 | 01, 02, 03, 04, 06, 07             | 11.40%        | | 19                  | 58981.02                          | 60%                 | 01, 02, 03, 04, 06, 07             | 11.40%        |
  
- +Based 
-The next figure shows the percentage of semantic problems solved by PORSCE-II with different values of the threshold and the performance +on these results, we selected the following optimal threshold //N// for each datasets: //N=1// for //Semantic-Matching 01//, //N=2// for //Semantic-Matching 02// and //03//, //N=3// for //Semantic-Matching 04//, //N=4// for //Semantic-Matching 07// and //N=6// for //Semantic-Matching 06//.
-measured by comparing the time taken by the algorithm to solve the //Semantic-Matching 01// with the different thresholds. As can be seen, +
-the computational cost increases (performance decreases) when the threshold is incremented. Thus, using a threshold of 10, the algorithm +
-obtains a performance close to 20%, which means that the algorithm is about 1/0.20=5 times slower than using a threshold of 1. Based +
-on these results, we selected the optimal thresholds //N// for each datasets: //N=1// for //Semantic-Matching 01//, //N=2// for //Semantic-Matching 02// and //03//, //N=3// for //Semantic-Matching 04//, //N=4// for //Semantic-Matching 07// and //N=6// for //Semantic-Matching 06//. Note that +
-ComposIT does not require any special configuration for the semantic datasets as it calculates matches at unlimited depth.  +
- +
-{{:inv:composit:porsce-thresholds.png?400|}} +
- +
  
 ^        Dataset        ^    Algorithm    ^      Solution      ^^                                        Execution (ms)                                        ^^^^^^^^ ^        Dataset        ^    Algorithm    ^      Solution      ^^                                        Execution (ms)                                        ^^^^^^^^
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 </code> </code>
 Where algorithm.jar is one of the available algorithms:  Where algorithm.jar is one of the available algorithms: 
-  * CompositAlgorithm.jar ([[http://apps.citius.usc.es/upload/?a=d&i=M5CkkF|download]]) +  * ComposIT: CompositAlgorithm.jar ([[http://apps.citius.usc.es/upload/?a=d&i=M5CkkF|download]]) 
-  * PorsceAlgorithm.jar ([[http://apps.citius.usc.es/upload/?a=d&i=Wv1RNM|download]]) +  * PORSCE-II: PorsceAlgorithm.jar ([[http://apps.citius.usc.es/upload/?a=d&i=Wv1RNM|download]]) 
-  * OWLSXplanAlgorithm.jar ([[http://apps.citius.usc.es/upload/?a=d&i=Re6Nv2|download]])+  * OWLS-Xplan: OWLSXplanAlgorithm.jar ([[http://apps.citius.usc.es/upload/?a=d&i=Re6Nv2|download]])
  
 +<note important>
 +These versions of the OWLS-Xplan and PORSCE-II were modified to support the integration with the test platform. Original versions
 +of these algorithms can be downloaded here:
 +  * PORSCE-II: http://www.dit.hua.gr/~raniah/research.html
 +  * OWLS-Xplan 2.0: http://projects.semwebcentral.org/projects/owls-xplan/
 +</note>
 You can launch also a background test from the command line, with the following syntax: You can launch also a background test from the command line, with the following syntax: