Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
The INFOMIX system for advanced integration of incomplete and inconsistent data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Internet-scale collection of human-reviewed data
Proceedings of the 16th international conference on World Wide Web
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Query language support for incomplete information in the MayBMS system
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
MCDB: a monte carlo approach to managing uncertain data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Approximating predicates and expressive queries on probabilistic databases
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Matchin: eliciting user preferences with an online game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MayBMS: a probabilistic database management system
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Collabio: a game for annotating people within social networks
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Improving search engines using human computation games
Proceedings of the 18th ACM conference on Information and knowledge management
Learning string transformations from examples
Proceedings of the VLDB Endowment
Corroborating information from disagreeing views
Proceedings of the third ACM international conference on Web search and data mining
On probabilistic fixpoint and Markov chain query languages
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sampling the repairs of functional dependency violations under hard constraints
Proceedings of the VLDB Endowment
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Providing support for full relational algebra in probabilistic databases
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Using Markov Chain Monte Carlo to play Trivia
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Proceedings of the VLDB Endowment
Declarative data fusion – syntax, semantics, and implementation
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Query optimization over crowdsourced data
Proceedings of the VLDB Endowment
Answering planning queries with the crowd
Proceedings of the VLDB Endowment
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Harnessing a crowd of users for the collection of mass data (data sourcing) has recently become a wide-spread practice. One effective technique is based on games as a tool that attracts the crowd to contribute useful facts. We focus here on the data management layer of such games, and observe that the development of this layer involves challenges such as dealing with probabilistic data, combined with recursive manipulation of this data. These challenges are difficult to address using current declarative data management framework works, and we thus propose here a novel such framework, and demonstrate its usefulness in expressing different aspects in the data management of Trivia-like games. We have implemented a system prototype with our novel data management framework at its core, and we highlight key issues in the system design, as well as our experimentations that indicate the usefulness and scalability of the approach.