  Intervals have been generated in many applications (e.g., temporal
databases), and they are often associated with weights, such as prices. This
paper addresses the problem of processing top-k weighted stabbing queries on
interval data. Given a set of weighted intervals, a query value, and a result
size $k$, this problem finds the $k$ intervals that are stabbed by the query
value and have the largest weights. Although this problem finds practical
applications (e.g., purchase, vehicle, and cryptocurrency analysis), it has not
been well studied. A state-of-the-art algorithm for this problem incurs
$O(n\log k)$ time, where $n$ is the number of intervals, so it is not scalable
to large $n$. We solve this inefficiency issue and propose an algorithm that
runs in $O(\sqrt{n }\log n + k)$ time. Furthermore, we propose an $O(\log n +
k)$ algorithm to further accelerate the search efficiency. Experiments on two
real large datasets demonstrate that our algorithms are faster than existing
algorithms.
