Abstract:The combination of web services is the result of complex and increasing needs of the users and disability of single web services in resolving the user's needs. One of the important challenges in the field of web 2.0 is the combination of web services based on their qualitative features. Since it is probable that there would be several different combinations of services for achieving a specific goal, choosing the service is based on some qualitative features like combining, availability, acceptability, service cost and security. One of the important issues is the quantitative survey of combining rate of the two services shared on the combination so that they have the ability to combine with each other, correctly. In order to measure the combining ability of services, in the first stage, the more number of effective factors on combining features of services are surveyed in comparison with the present methods. In the second stage, metric is introduced for the effective factors, and in the third stage, an appropriate weight for each factor is found and finally, based on their relationships with each other, a more accurate rate of combining is obtained.

Abstract:The combination of web services is the result of complex and increasing needs of the users and disability of single web services in resolving the users' needs. One of the important challenges in the field of web 2.0 is the combination of web services based on their qualitative features. Since it is probable that there would be several different combinations of services for achieving a specific goal, choosing the service is based on some qualitative features like combining, availability, acceptability, service cost and security. One of the important issues is the quantitative survey of combining rate of the two services shared on the combination. So in this research, in the first stage, for measuring the combining rate, the effective factors on this feature would be surveyed. In the second stage, for choosing the optimum service based on the qualitative feature of combining, the local strategy is used. The proposed algorithm in local strategy selects services that their combining rate is more than a specific threshold. The implementations and analysis show that the proposed algorithm presents the optimum service in user's view with an acceptable combining capability. Also, the analysis of results and an evaluation with a case study show the optimized results of the local proposed algorithms compared to existing methods.