Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data (Full Version)

TL;DR

This paper introduces more efficient algorithms for top-k weighted stabbing queries on interval data, significantly improving scalability and speed over previous methods, with proven effectiveness on large real datasets.

Contribution

It presents novel algorithms that reduce the query time complexity from O(n log k) to O(√n log n + k) and O(log n + k), enhancing scalability for large datasets.

Findings

Algorithms outperform existing methods in speed.

Proven effectiveness on large real datasets.

Significant scalability improvements.

Abstract

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.