Building the Data Warehouse
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
TurKit: tools for iterative tasks on mechanical Turk
Proceedings of the ACM SIGKDD Workshop on Human Computation
The anatomy of a large-scale human computation engine
Proceedings of the ACM SIGKDD Workshop on Human Computation
Crowdsourcing systems on the World-Wide Web
Communications of the ACM
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
The jabberwocky programming environment for structured social computing
Proceedings of the 24th annual ACM symposium on User interface software and technology
Human Computation
Max algorithms in crowdsourcing environments
Proceedings of the 21st international conference on World Wide Web
Answering search queries with CrowdSearcher
Proceedings of the 21st international conference on World Wide Web
Strategies for crowdsourcing social data analysis
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Deco: a system for declarative crowdsourcing
Proceedings of the VLDB Endowment
Crowd IQ: measuring the intelligence of crowdsourcing platforms
Proceedings of the 3rd Annual ACM Web Science Conference
Proceedings of the 3rd Annual ACM Web Science Conference
An introduction to human computation and games with a purpose
ICWE'13 Proceedings of the 13th international conference on Web Engineering
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An essential aspect for building effective crowdsourcing com- putations is the ability of "controlling the crowd", i.e. of dynamically adapting the behaviour of the crowdsourcing systems as response to the quantity and quality of completed tasks or to the availability and reliability of performers. Most crowdsourcing systems only provide limited and predefined controls; in contrast, we present an approach to crowdsourcing which provides fine-level, powerful and flexible controls. We model each crowdsourcing application as composition of elementary task types and we progressively transform these high level specifications into the features of a reactive execution environment that supports task planning, assignment and completion as well as performer monitoring and exclusion. Controls are specified as active rules on top of data structures which are derived from the model of the application; rules can be added, dropped or modified, thus guaranteeing maximal flexibility with limited effort. We also report on our prototype platform that implements the proposed framework and we show the results of our experimentations with different rule sets, demonstrating how simple changes to the rules can substantially affect time, effort and quality involved in crowdsourcing activities.