Publication | Closed Access
CDB
65
Citations
64
References
2017
Year
Unknown Venue
Crowd ComputingCrowd-powered OperationsComputational Social ScienceTree ModelInformation RetrievalData ScienceEngineeringVery Large DatabaseManagementData IntegrationComputer ScienceDatabase SystemsCrowdsourcingCombinatorial OptimizationHuman ComputationData ManagementMechanism DesignQuery Optimization
Crowdsourcing database systems have been proposed to leverage crowd-powered operations to encapsulate the complexities of interacting with the crowd. Existing systems suffer from two major limitations. Firstly, in order to optimize a query, they often adopt the traditional tree model to select an optimized table-level join order. However, the tree model provides a coarse-grained optimization, which generates the same order for different joined tuples and limits the optimization potential that different joined tuples can be optimized by different orders. Secondly, they mainly focus on optimizing the monetary cost. In fact, there are three optimization goals (i.e., smaller monetary cost, lower latency, and higher quality) in crowdsourcing, and it calls for a system to enable multi-goal optimization.
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