In this paper, we attempt to approximate and index a ddimensional (d ≥ 1) spatio-temporal trajectory with a low order continuous polynomial. There are many possible ways to choose the polynomial, including (continuous) Fourier transforms, splines, non-linear regression, etc. Some of these possibilities have indeed been studied before. We hypothesize that one of the best possibilities is the polynomial that minimizes the maximum deviation from the true value, which is called the minimax polynomial. Minimax approximation is particularly meaningful for indexing because in a branch-and-bound search (i.e., for finding nearest neighbours), the smaller the maximum deviation, the more pruning opportunities there exist. However, in general, among all the polynomials of the same degree, the optimal minimax polynomial is very hard to compute. However, it has been shown that the Chebyshev approximation is almost identical to the optimal minimax polynomial, and is easy to compute [16] . Thus, in this paper, we explore how to use the Chebyshev polynomials as a basis for approximating and indexing d-dimensional trajectories. The key analytic result of this paper is the Lower Bounding Lemma. That is, we show that the Euclidean distance between two d-dimensional trajectories is lower bounded by the weighted Euclidean distance between the two vectors of Chebyshev coefficients. This lemma is not trivial to show, and it ensures that indexing with Chebyshev coefficients admits no false negatives. To complement the analytic result, we conducted comprehensive experimental evaluation with real and generated 1-dimensional to 4-dimensional data sets. We compared the proposed scheme with the Adaptive Piecewise Constant Approximation (APCA) scheme. Our preliminary results indicate that in all situations we tested, Chebyshev indexing dominates APCA in pruning power, I/O and CPU costs.
REF approximates trajectories with Chebyshev Polynomials, then compute the distance based on the polynomials.
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04
Computer Science