Authors: G. Kollios, D. Gunopulos, V.J. Tsotras, A. Delis, M. Hadjieleftheriou
Title: Indexing Animated Objects Using Spatiotemporal Access Methods
Conference: IEEE Transactions on Knowledge & Data Engineering (TKDE) Vol. 13, No. 5, September/October 2001
Abstract: 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 spatiotemporal
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 $S$ between frames Fi and Fj", as well as
nearest neighbor queries like: "find the q nearest objects to a given
position A between frames Fi and Fj". 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.