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Spatio-Temporal Stream Processing
[chapter]
2009
Encyclopedia of Database Systems
We propose an unsupervised stream processing framework that learns a Bayesian representation of observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a causal Bayesian graph. This allows the model to be efficient through compactness and sparsity in the causal graph, and to provide probabilities at any level of abstraction for activities or chains of activities. Methods and ideas
doi:10.1007/978-0-387-39940-9_3665
fatcat:mivvr7iy6jgmrmjmijhmppougq