Distributed and Parallel Databases
Quality driven web services composition
WWW '03 Proceedings of the 12th international conference on World Wide Web
QoS Aggregation for Web Service Composition using Workflow Patterns
EDOC '04 Proceedings of the Enterprise Distributed Object Computing Conference, Eighth IEEE International
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Journal of Systems and Software
A Genetic Algorithms-Based Approach for Optimized Self-protection in a Pervasive Service Middleware
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
QoS-aware service composition in dynamic service oriented environments
Middleware'09 Proceedings of the ACM/IFIP/USENIX 10th international conference on Middleware
Seeking Quality of Web Service Composition in a Semantic Dimension
IEEE Transactions on Knowledge and Data Engineering
QoS-Aware Automatic Service Composition by Applying Functional Clustering
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
Efficient Heuristic Approach with Improved Time Complexity for Qos-Aware Service Composition
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
Bridging the Gap between Semantic Web Service Composition and Common Implementation Architectures
SCC '11 Proceedings of the 2011 IEEE International Conference on Services Computing
Towards robust service compositions in the context of functionally diverse services
Proceedings of the 21st international conference on World Wide Web
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The Service-Oriented Computing (SOC) paradigm envisions the composition of loosely coupled services to build complex applications. Most current selection algorithms assume that all services assigned to a certain task provide exactly the same functionality. However, in realistic settings larger groups of services exist that share the same purpose, yet provide a slightly different interface. Incorporating these services increases the number of potential solutions, but also includes functional invalid configurations, resulting in a sparse solution space. As a consequence, applying naïve heuristic algorithms leads to poor results by reason of the increased probability of local optima. For that purpose, we propose a functionality clustering in order to leverage background knowledge on the compatibility of the services. This enables heuristic algorithms to discover valid workflow configurations in shorter time. We integrate our approach into a genetic algorithm by performing repair operations on invalid genomes. In the evaluation we compare our approach with related heuristic algorithms that use the same guided target function but pick services in a random manner.