Proceedings of the 10th ACM workshop on Web information and data management
Proceedings of the VLDB Endowment
Towards health 2.0: mashups to the rescue
NGITS'09 Proceedings of the 7th international conference on Next generation information technologies and systems
InEDvance: advanced IT in support of emergency department management
NGITS'09 Proceedings of the 7th international conference on Next generation information technologies and systems
Advancing search query autocompletion services with more and better suggestions
ICWE'10 Proceedings of the 10th international conference on Web engineering
A framework for dynamic data source identification and orchestration on the web
Proceedings of the 3rd and 4th International Workshop on Web APIs and Services Mashups
A new approach to performance optimization of mashups via data flow refactoring
Proceedings of the Second Asia-Pacific Symposium on Internetware
Database-as-a-service for long-tail science
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
VizDeck: self-organizing dashboards for visual analytics
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
EnglishMash: usability design for a natural mashup composition environment
ICWE'12 Proceedings of the 12th international conference on Current Trends in Web Engineering
An efficient and scalable ranking technique for mashups involving RSS data sources
Journal of Network and Computer Applications
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A mashup is a Web application that integrates data, computation and GUI provided by several systems into a unique tool. The concept originated from the understanding that the number of applications available on the Web and the need for combining them to meet user requirements, are growing very rapidly. This demo presents MatchUp, a system that supports rapid, on-demand, intuitive development of mashups, based on a novel autocompletion mechanism. The key observation guiding the development of MatchUp is that mashups developed by different users typically share common characteristics; they use similar classes of mashup components and glue them together in a similar manner. MatchUp exploits these similarities to predict, given a user's partial mashup specification, what are the most likely potential completions (missing components and connection between them) for the specification. Using a novel ranking algorithm, users are then offered top-k completions from which they choose and refine according to their needs.