Infomaster: an information integration system
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Heuristic and randomized optimization for the join ordering problem
The VLDB Journal — The International Journal on Very Large Data Bases
MiniCon: A scalable algorithm for answering queries using views
The VLDB Journal — The International Journal on Very Large Data Bases
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
PRIVATE-IYE: A Framework for Privacy Preserving Data Integration
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Enterprise information mashups: integrating information, simply
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mashup Feeds: continuous queries over web services
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Declarative Data Services: This Is Your Data on SOA
SOCA '07 Proceedings of the IEEE International Conference on Service-Oriented Computing and Applications
Deploying and managing Web services: issues, solutions, and directions
The VLDB Journal — The International Journal on Very Large Data Bases
UQBE: uncertain query by example for web service mashup
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Run-Time Monitoring for Privacy-Agreement Compliance
ICSOC '07 Proceedings of the 5th international conference on Service-Oriented Computing
Enterprise Mashups: Design Principles towards the Long Tail of User Needs
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Mashup Advisor: A Recommendation Tool for Mashup Development
ICWS '08 Proceedings of the 2008 IEEE International Conference on Web Services
CAFISE-S: An Approach to Deploying SOA in Scientific Information Integration
ICWS '08 Proceedings of the 2008 IEEE International Conference on Web Services
Service-Oriented Architecture for Privacy-Preserving Data Mashup
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
SOA-Based Integration of the Internet of Things in Enterprise Services
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
Bringing semantic annotations to web services: OWL-S from the SAWSDL perspective
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Semantic-Based Mashup of Composite Applications
IEEE Transactions on Services Computing
A Query Rewriting Approach for Web Service Composition
IEEE Transactions on Services Computing
End-to-End Service Support for Mashups
IEEE Transactions on Services Computing
Seeking Quality of Web Service Composition in a Semantic Dimension
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Data Mashup
AINA '11 Proceedings of the 2011 IEEE International Conference on Advanced Information Networking and Applications
Automatic Web Service Composition with a Heuristic-Based Search Algorithm
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
Fine-grained access control for cloud computing
International Journal of Grid and Utility Computing
FASER Formal and Automatic Security Enforcement by Rewriting by BPA algebra with test
International Journal of Grid and Utility Computing
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Data mashup is an important class of the situational applications that combines information on the fly from multiple data sources to respond to immediate business data needs. Mashing-up data requires important programming skills on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers. In this paper, we propose a declarative approach for mashing-up data. The approach allows the mashups' creators to create data mashups without any programming involved, they just need to specify 'declaratively' their data needs. The approach exploits the mature query rewriting techniques to build the mashups automatically while taking into account the data's privacy and security concerns. We apply the proposed approach to the healthcare domain, and report a thorough experimental evaluation. The reported results are very promising.