Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data

Daichi Amagata, Junya Yamada, Yuchen Ji, Takahiro Hara

Abstract: This paper addresses the problem of processing top-k weighted stabbing queries on interval data. 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.