Massive datasets of curves, such as time series and trajectories, are continuously generated by mobile and sensing devices. A relevant operation on curves is similarity search : given a dataset S of curves , construct a data structure that, for any query curve q, finds the curves in S similar to q. Similarity search is a computational demanding task, in particular when a robust distance function is used, such as the continuous Fr\'echet distance. In this paper, we propose FRESH, a novel approximate solution to find similar curves under the continuous Fr\'echet distance. FRESH leverages on a locality sensitive hashing scheme for detecting candidate near neighbors of the query curve, and on a subsequent pruning step based on a cascade of curve simplifications. By relaxing the requirement of\fresh  exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach. Furthermore, the improvement peaks up to two orders of magnitude, and even more, by relaxing the precision .
This paper studies the r-range search problem for curves under the continuous Fr\'echet distance : given a dataset S of n polygonal curves and a threshold r>0 , construct a data structure that, for any query curve q, efficiently returns all entries in S with distance at most r from q. We propose FRESH, an approximate and randomized approach for r-range search, that leverages on a locality sensitive hashing scheme for detecting candidate near neighbors of the query curve, and on a subsequent pruning step based on a cascade of curve simplifications. We experimentally compare\fresh to exact and deterministic solutions, and we show that high performance can be reached by suitably relaxing precision and recall .
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Massive datasets of curves, such as time series and trajectories, are continuously generated by mobile and sensing devices. A relevant operation on curves is similarity search
This paper studies the r-range search problem for curves under the continuous Fr\'echet distance
R
curves
n polygonal curves and a threshold r>0
R
finds the curves in S similar to q. Similarity search is a computational demanding task, in particular when a robust distance function is used, such as the continuous Fr\'echet distance. In this paper, we
efficiently returns all entries in S with distance at most r from q. We
R
a novel approximate solution to find similar curves under the continuous Fr\'echet distance. FRESH
an approximate and randomized approach for r-range search, that
R
By relaxing the requirement of
We experimentally compare
A
to
R
FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and
and we show that high performance can be reached by suitably relaxing
R
100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach. Furthermore, the improvement peaks up to two orders of magnitude, and even more, by relaxing the precision
and recall