Indexing Animated Objects Using Spatiotemporal Access Methods

We present a new approach for indexing animated objects and efficiently answering queries about their position in time and space. In particular, we consider an animated movie as a evolution. A movie is viewed as an ordered sequence of frames, where each frame is a 2-dimensional space occupied by the objects that appear in that frame. The queries of interest are range queries of the form: “find the objects that appear in area between frames and ”, as well as nearest neighbor queries like: “find the nearest objects to a given position between frames and ”. The straightforward approach to index such objects considers the frame sequence as another dimension and uses a 3-dimensional access method (like an R-Tree or its variants). This however assigns long “lifetime” intervals to objects that appear through many consecutive frames. Long intervals are difficult to cluster efficiently in a 3-dimensional index. Instead, we propose to reduce the problem to a partial-persistence problem. Namely, we use a 2-dimensional access method that is made partially persistent. We show that this approach leads to faster query performance while still using storage proportional to the total number of changes in the frame evolution. What differentiates this problem from traditional temporal indexing approaches is that objects are allowed to move and/or change their extent continuously between frames. We present novel methods to approximate such object evolutions. We formulate an optimization problem for which we provide an optimal solution for the case where objects move linearly. Finally, we present an extensive experimental study of the proposed methods. While we concentrate on animated movies, our approach is general and can be applied to other spatiotemporal applications as well.