Journal of the American Society for Information Science and Technology
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Current Solutions for Web Service Composition
IEEE Internet Computing
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Community-Based Service Discovery
ICWS '06 Proceedings of the IEEE International Conference on Web Services
Investigating web services on the world wide web
Proceedings of the 17th international conference on World Wide Web
On the functional quality of service (FQoS) to discover and compose interoperable web services
Expert Systems with Applications: An International Journal
Combining global optimization with local selection for efficient QoS-aware service composition
Proceedings of the 18th international conference on World wide web
Discovering Homogeneous Web Service Community in the User-Centric Web Environment
IEEE Transactions on Services Computing
A Framework for Dynamic Service Discovery
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Selecting skyline services for QoS-based web service composition
Proceedings of the 19th international conference on World wide web
An Approach for Mining Web Service Composition Patterns from Execution Logs
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Combining Local Optimization and Enumeration for QoS-aware Web Service Composition
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Survey of clustering algorithms
IEEE Transactions on Neural Networks
International Journal of Web Services Research
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The web has undergone a tremendous shift from information repository to the provisioning capacity of services. As an effective means of constructing coarse-grained solutions by dynamically aggregating a set of services to satisfy complex requirements, traditional service composition suffers from dramatic decrease on the efficiency of determining the optimal solution when large scale services are available in the Internet based service market. Most current approaches look for the optimal composition solution by real-time computation, and the composition efficiency greatly depends on the adopted algorithms. To eliminate such deficiency, this paper proposes a semi-empirical composition approach which incorporates the extraction of empirical evidence from historical experiences to provide guidance to solution space reduction to real-time service selection. Service communities and historical requirements are further organized into clusters based on similarity measurement, and then the probabilistic correspondences between the two types of clusters are identified by statistical analysis. For each new request, its hosting requirement cluster would be identified and corresponding service clusters would be determined by leveraging Bayesian inference. Concrete services would be selected from the reduced solution space to constitute the final composition. Timing strategies for re-clustering and consideration to special cases in clustering ensures continual adaption of the approach to changing environment. Instead of relying solely on pure real-time computation, the approach distinguishes from traditional methods by combining the two perspectives together.