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inv:composit:validation [2013/02/28 18:20] – [Evaluation] pablo.rodriguez.mierinv:composit:validation [2013/02/28 18:22] – [Evaluation] pablo.rodriguez.mier
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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         |
  
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-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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 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)                                        ^^^^^^^^