Research On The Key Issues Of Service Composition Towards Fuzzy Request

With the improvement of technology and the popularity of network applications, on one hand, more and more companies issue their business function units to the network by the form of Web services. On the other hand, the general consumer presents higher demands to network services. They are no longer satisfied with the simple service providing approaches, such as getting weather information, inquiring the flight information and reserving a room in a hotel. Many users expect Internet service platform allow them to make fuzzy requests using natural language expression. Wherever they are, users can enjoy their specially tailored, high-quality service which can provide composite function. This poses a challenge to the current Web service product delivery mechanisms. Obviously, users can’t get faultless Service from any businesses. In an increasingly competitive commercial market context, to meet users’ service demand, we need every business entity in the same commercial value chain to set up dynamic virtual enterprise alliance, integrate their respective advantage resources, develop collaborative commerce, organically integrate web services published by union members, then form coarser-grained composite service with internal process logic. Just in this way the constantly changing user demand could be dynamically、flexibly and rapidly respond. So we research the key issues in service composition orienting vague requests.Considering the features of instantaneity, customization, fuzzification user service requests appears, this article delves into Web service discovery and matching related to the whole life cycle of service composition, automatic combination, dynamic service selection, and the monitoring and exception handling about process execution. We achieved the following results:1. We proposed service discovery and mapping methods based on fuzzy request. Compared with the traditional matching methods, the service discovery and matching scheme not only allows the user to make vague service requests, but also improve the recall and precision rate. By fuzzy expanding traditional ontology model, fuzzy ontology model was constructed including fuzzy domain ontology and fuzzy QoS ontology. With the support of fuzzy ontology, we divided the service into two layers (functional semantic matching layer and non-functional properties semantic matching layer) to match and gave the service matching algorithm.2. We proposed a mapping algorithm from OWL-S process model to flow calculus stream. On this basis, we gave flow-calculus-based automatic Web service composition methods. When the room planed room for composite service process is larger, the complexity of algorithms is higher. This method solved the problem abovementioned and improved the efficiency of service composition. With the support of domain ontology, we use the planning and reasoning ability of flow calculations to automatically generate abstract composite service process. The BCA-FC algorithms uses the progression reasoning mechanisms to reasoning the actions and states. Compared to the other AI planning methods using regression mechanism, BCA-FA algorithms reduces the computational complexity when valuating properties. Experimental results show that it still has high efficiency when there are a large number of task nodes in the combined service process.3. We proposed a dynamic service selection model based on multi-dimensional QoS constraints, providing multi-dimensional QoS support for service selection and dynamic service replacement in process execution. We proposed a heuristics services selection algorithms based on MMKP knapsack problem. This algorithm reduce the search space size, and complete service selection quickly and effectively. As current study hasn’t explicit a comprehensive evaluation standard about composite service QoS, and not fully considered user requests especially the fuzzy requests, we proposed a service QoS measurement model, defined service quality effectiveness evaluation function, and gave dynamic service selection model in different global QoS constraints, providing multi-dimensional QoS support for service selection and dynamic service replacement in process execution. We proposed a heuristic selection approach using rule bank based on MMKP knapsack problem. Experiments show that by using the heuristic rule base selection algorithm reduces the search space size, and can complete service selection quickly and effectively. This method can satisfy users’ QoS fuzzy constraints, in the main time, make it possible to obtain the composite service process instance with the best comprehensive performance evaluation.4. We proposed an exception handling model of process, effectively implementing the rapid diagnosis of anomalies in the business process layer. We proposed exceptional handling methods of process based on business logic layer, improving the automation level and adaptive ability of exception handling during the execution of composite service process. In the state monitoring module of exception handling model, we obtained timely the information resources closely related to service execution through message published and subscribed mechanism. Accordingly, we can make diagnosis and hence set basis for possible exception handling of process. Based on generation figure of revoked business, we used exception handling methods of composite service process. This method enables us to withdraw service in an acceptable cost range in the context of use, guaranteeing the reliable and consistent execution of composite service process.