To make service matchmaking more adaptive to various service requests and diverse web services, an adaptive approach-ASMA is proposed to service matchmaking based on temporal logic model-checking. The approach is base...To make service matchmaking more adaptive to various service requests and diverse web services, an adaptive approach-ASMA is proposed to service matchmaking based on temporal logic model-checking. The approach is based on the proposed abstract service model, ASM-TL, which addresses some important constraints for identifying capabilities of web services, such as service inner constraints and invocation constraints, and also has a virtual process model for describing service behavioral properties. By treating service requests as temporal logic conditions and web services as temporal models, ASMA does service matchmaking through model checking. Therefore, ASMA makes service matchmaking more accurate and more adaptive to the variety of service requests and the diversity of web services. The approach has been applied to the problem solving environment （PSE） for bioinformatics research. Applications show that the approach is suitable for dynamic environments.展开更多
Semantic Service Matchmaking(SSM)can be leveraged for mining the most suitable service to accommodate a diversity of user demands.However,existing research on SSM mostly involves logical or non-logical matching,leadin...Semantic Service Matchmaking(SSM)can be leveraged for mining the most suitable service to accommodate a diversity of user demands.However,existing research on SSM mostly involves logical or non-logical matching,leading to unavoidable false-positive and false-negative problems.Combining different types of SSM methods is an effective way to improve this situation,but the adaptive combination of different service matching methods is still a difficult issue.To conquer this difficulty,a hybrid SSM method,which is based on a random forest and combines the advantages of existing SSM methods,is proposed in this paper.The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest,converting the service matching into a two classification problem.Therefore,our method avoids the flaws found in manual threshold setting.Experimental results show that the proposed method achieves an outstanding performance.展开更多
基金The National High Technology Research and Devel-opment Program of China (863Program) (No2006AA12Z202)the National Natural Science Foundation of China (No90412010)
文摘To make service matchmaking more adaptive to various service requests and diverse web services, an adaptive approach-ASMA is proposed to service matchmaking based on temporal logic model-checking. The approach is based on the proposed abstract service model, ASM-TL, which addresses some important constraints for identifying capabilities of web services, such as service inner constraints and invocation constraints, and also has a virtual process model for describing service behavioral properties. By treating service requests as temporal logic conditions and web services as temporal models, ASMA does service matchmaking through model checking. Therefore, ASMA makes service matchmaking more accurate and more adaptive to the variety of service requests and the diversity of web services. The approach has been applied to the problem solving environment （PSE） for bioinformatics research. Applications show that the approach is suitable for dynamic environments.
基金the National Natural Science Foundation of China(Nos.61872104 and 61502118)the National Science and Technology Major Project of China(No.2016ZX03001023-005)the Natural Science Foundation of Heilongjiang Province in China(No.F2016009)。
文摘Semantic Service Matchmaking(SSM)can be leveraged for mining the most suitable service to accommodate a diversity of user demands.However,existing research on SSM mostly involves logical or non-logical matching,leading to unavoidable false-positive and false-negative problems.Combining different types of SSM methods is an effective way to improve this situation,but the adaptive combination of different service matching methods is still a difficult issue.To conquer this difficulty,a hybrid SSM method,which is based on a random forest and combines the advantages of existing SSM methods,is proposed in this paper.The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest,converting the service matching into a two classification problem.Therefore,our method avoids the flaws found in manual threshold setting.Experimental results show that the proposed method achieves an outstanding performance.
