≪ View Page

Diferenzas

Isto amosa as diferenzas entre a revisión seleccionada e a versión actual da páxina.

Enlazar a esta vista de comparación

Both sides previous revisionPrevious revision
Next revision		Previous revision
inv:composit:validation [2013/02/28 18:20] – [Evaluation] pablo.rodriguez.mierinv:composit:validation [2013/02/28 18:31] (actual) – pablo.rodriguez.mier
Liña 3: Liña 3:
  
  
-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
Liña 75: Liña 75:
 | 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
Liña 169: Liña 166:
  
 Based Based
-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//. Note that+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//.
  
 ^        Dataset        ^    Algorithm    ^      Solution      ^^                                        Execution (ms)                                        ^^^^^^^^ ^        Dataset        ^    Algorithm    ^      Solution      ^^                                        Execution (ms)                                        ^^^^^^^^
Liña 226: Liña 223:
 </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: