One of the greatest challenges in spatial crowdsourcing is determining the veracity of reports from multiple users about a particular event or phenomenon. In this paper, we address the difficulties of truth discovery in spatio-temporal tasks and present a new method based on recursive Bayesian estimation (BE) from multiple reports of users. Our method incorporates a reliability model for users, which improves as more reports arrive while increasing the accuracy of the model in labeling the state of the event. The model is further improved by Kalman estimation (BE+KE) that models the spatio-temporal correlations of the events and predicts the next state of an event and is corrected when new reports arrive. The methods are tested in a simulated environment, as well as using real-world data. Experimental results show that our methods are adaptable to the available data, can incorporate previous beliefs, and outperform existing truth discovery methods of spatio-temporal events.
Garcia-Ulloa REF proposed a a dynamic graphical model for spatio-temporal event discovery.
Truth Discovery for SpatioTemporal Events from Crowdsourced Data
PVLDB
PVLDB
Computer Science