Abstract
Skyline queries have gained attention lately for supporting effective retrieval over massive spatial data. While efficient algorithms have been studied for spatial skyline queries using Euclidean distance, or, L 2 norm, these algorithms are (1) still quite computationally intensive and (2) unaware of the road constraints. Our goal is to develop a more efficient algorithm for L 1 norm, also known as Manhattan distance, which closely reflects road network distance for metro areas with well-connected road networks. Towards this goal, we present a simple and efficient algorithm which, given a set P of data points and a set Q of query points in the plane, returns the set of spatial skyline points in just O(|P|log|P|) time, assuming that |Q| ≤ |P|. This is significantly lower in complexity than the best known method. In addition to efficiency and applicability, our proposed algorithm has another desirable property of independent computation and extensibility to L ∞ norm, which naturally invites parallelism and widens applicability. Our extensive empirical results suggest that our algorithm outperforms the state-of-the-art approaches by orders of magnitude.
Work by Son and Ahn was supported by National IT Industry Promotion Agency (NIPA) under the program of Software Engineering Technologies Development and Experts Education. Work by Hwang was supported by Microsoft Research Asia.
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Son, W., Hwang, Sw., Ahn, HK. (2011). MSSQ: Manhattan Spatial Skyline Queries. In: Pfoser, D., et al. Advances in Spatial and Temporal Databases. SSTD 2011. Lecture Notes in Computer Science, vol 6849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22922-0_19
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