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Data mining of spatio-temporal objects

Speaker:Peter Revesz


University:University of Nebraska, Lincoln

Room :Α56



We consider spatio-temporal objects in a broad sense of anything whose shape can be represented in n-dimensional space at all times but varies over time. The most challenging problems regarding spatio-temporal objects are in the interpolation and extrapolation of their trajectories rather than a mere recording of their trajectories. In this talk, we give an algorithmic solution to the problem of inertial navigation, which is the estimation of the current location and direction of a moving object given past periodic measurements of its acceleration measured by an accelerometer. We also describe some recent data mining results on other spatio-temporal objects, including an algorithm to predict many years ahead the citation curves of individual researchers

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