Proceedings of the 17th International Conference on Data Engineering
Enterprise information mashups: integrating information, simply
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Declarative Data Services: This Is Your Data on SOA
SOCA '07 Proceedings of the IEEE International Conference on Service-Oriented Computing and Applications
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Incomplete Preference-driven Web Service Selection
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 1
Top-k dominant web services under multi-criteria matching
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Effective and Flexible NFP-Based Ranking of Web Services
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
Combining Quality of Service and Social Information for Ranking Services
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
Computing Service Skyline from Uncertain QoWS
IEEE Transactions on Services Computing
A Query Rewriting Approach for Web Service Composition
IEEE Transactions on Services Computing
A fuzzy framework for selecting top-k web service compositions
ACM SIGAPP Applied Computing Review
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Data as a Service (DaaS) is a flexible way that allows enterprises to expose their data. Composition of DaaS services provides bridges to answer queries. User preferences are becoming increasingly important to personalizing the composition process. In this paper, we propose an approach to compose DaaS services in the context of preference queries where preferences are modeled by means of fuzzy sets that allow for a large variety of flexible terms such as 'cheap', 'affordable' and 'fairly expensive'. The proposed approach is based on RDF-based query rewritings to take into account the partial matching between individual DaaS services and parts of the user query. Matching degrees between DaaS services and fuzzy preference constraints are computed by means of different constraints inclusion methods. Such degrees express to which extent a service is relevant to the resolution of the query. A fuzzification of Pareto dominance is also proposed to better rank composite services by computing the score of services. The resulting scores are then used to compute the top-k DaaS service compositions that cover the user query.