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We propose a framework for querying a distributed database of video surveillance data in order to retrieve a set of likely paths of a person moving in the area under surveillance. In our framework, each camera of the surveillance system locally processes the data and stores video sequences in a storage unit and the metadata for each detected person in the distributed database. A pedestrian's path is formulated as a dynamic Bayesian network (DBN) to model the dependencies between subsequent observations of the person as he makes his way through the camera network. We propose a tool by which the analyst can pose queries about where a certain person appeared while moving in the site during a specified temporal window. The DBN is used in an algorithm that finds potentially relevant metadata records from the distributed databases and then assembles these into probable paths that the person took in the camera network. Finally, the system presents the analyst with the retrieved set of likely paths in ranked order. The computational complexity for our method is quadratic in the number of camera nodes and linear in the number of moving persons. Experiments were carried out on simulated data to test the system with large distributed databases and in a real setting in which six databases store the data from six video cameras. The simulations confirm that our method provides good results with varying numbers of cameras and persons moving in the network. In a real setting, the method reconstructs paths across the camera network with approximatively 75% accuracy at rank 1.