Spatio-Temporal Stream Processing [chapter]

Rodney Topor, Kenneth Salem, Amarnath Gupta, Kazuo Goda, Johannes Gehrke, Nathaniel Palmer, Mohamed Sharaf, Alexandros Labrinidis, John F. Roddick, Ariel Fuxman, Renée J. Miller, Wang-Chiew Tan (+205 others)
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
more » ... from a wide range of previous work are combined and interact to provide a uniform way to tackle a variety of common problems related to learning, classifying and predicting activities. We discuss how to use this framework to perform prediction of future activities and to generate events.
doi:10.1007/978-0-387-39940-9_3665 fatcat:mivvr7iy6jgmrmjmijhmppougq