The Fr\'{e}chet distance is one of the most studied distance measures between curves $P$ and $Q$. The data structure variant of the problem is a longstanding open problem: Efficiently preprocess $P$, so that for any $Q$ given at query time, one can efficiently approximate their Fr\'{e}chet distance. There exist conditional lower bounds that prohibit $(1 + \varepsilon)$-approximate Fr\'{e}chet distance computations in subquadratic time, even when preprocessing $P$ using any polynomial amount of time and space. As a consequence, the problem has been studied under various restrictions: restricting $Q$ to be a (horizontal) segment, or requiring $P$ and $Q$ to be so-called \emph{realistic} input curves. We give a data structure for $(1+\varepsilon)$-approximate discrete Fr\'{e}chet distance in any metric space $\mathcal{X}$ between a realistic input curve $P$ and any query curve $Q$. After preprocessing the input curve $P$ (of length $|P|=n$) in $O(n \log n)$ time, we may answer queries specifying a query curve $Q$ and an $\varepsilon$, and output a value $d(P,Q)$ which is at most a $(1+\varepsilon)$-factor away from the true Fr\'{e}chet distance between $Q$ and $P$. Thus, we give the first data structure that adapts to $\varepsilon$-values specified at query time, and the first data structure to handle query curves with arbitrarily many vertices. Our query time is asymptotically linear in $|Q|=m$, $\frac{1}{\varepsilon}$, $\log n$, and the realism parameter $c$ or $\kappa$. The method presented in this paper simplifies and generalizes previous contributions to the static problem variant. We obtain efficient queries (and therefore static algorithms) for Fr\'{e}chet distance computation in high-dimensional spaces and other metric spaces (e.g., when $\mathcal{X}$ is a graph under the shortest path metric). Our method supports subcurve queries at no additional cost.

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