Indexing animated objects using spatiotemporal access methods

We present a new approach for indexing animated objects and efficiently ans wering queries about their position in time and space. In particular, we co nsider an animated movie as a spatiotemporal evolution. A movie is viewed a s an ordered sequence of frames, where each frame is a 2D space occupied by the objects that appear in that frame. The queries of interest are range q ueries of the form, "find the objects that appear in area S between frames f(i) and f(j)" as well as nearest neighbor queries such as, "find the q nea rest objects to a given position A between frames f(i) and f(j)." The strai ghtforward approach to index such objects considers the frame sequence as a nother dimension and uses a 3D access method (such as, an R-Tree or its var iants), This, however, assigns long "lifetime" intervals to objects that ap pear through many consecutive frames. Long intervals are difficult to clust er efficiently in a 3D index. Instead, we propose to reduce the problem to a partial-persistence problem. Namely, we use a 2D access method that is ma de partially persistent. We show that this approach leads to faster query p erformance while still using storage proportional to the total number of ch anges in the frame evolution. What differentiates this problem from traditi onal temporal indexing approaches Is that objects are allowed to move and/o r change their extent continuously between frames. We present novel methods to approximate such object evolutions. We formulate an optimization proble m for which we provide an optimal solution for the case where objects move linearly. Finally, we present an extensive experimental study of the propos ed methods. While we concentrate on animated movies, our approach is genera l and can be applied to other spatiotemporal applications as well.