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.
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Notes
- 1.
Some applications may prefer smaller weights, and our algorithms can deal with this case.
- 2.
This idea is not available for the interval tree structure. This is because the interval tree structure does not guarantee that all intervals maintained in a node are stabbed by a given query.
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Acknowledgements
This work was partially supported by AIP Acceleration Research JPMJCR23U2, JST, and JSPS KAKENHI Grant Number 24K14961.
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Amagata, D., Yamada, J., Ji, Y., Hara, T. (2024). Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data. In: Strauss, C., Amagasa, T., Manco, G., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2024. Lecture Notes in Computer Science, vol 14910. Springer, Cham. https://doi.org/10.1007/978-3-031-68309-1_12
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DOI: https://doi.org/10.1007/978-3-031-68309-1_12
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