Abstract
In this paper, we study a new type of spatial query, namely aggregate k farthest neighbor (AkFN) search. Given a data point set P, a query point set Q, an AkFN query returns k points in P with the largest aggregate distances to all points in Q. For instance, it is reasonable to build a new hotel where the aggregate distances to all existing hotels are maximized to reduce competition. Our investigation of AkFN queries focuses on three aggregate functions, namely Sum, Max and Min. Assuming that the data set is indexed by R-tree, we propose two algorithms, namely minimum bounding (MB) and best first (BF), for efficiently solving AkFN queries with all three aggregate functions. The BF algorithm is incremental and IO optimal. Extensive experiments on both synthetic and real data sets confirm the efficiency and effectiveness of our proposed algorithms.
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Gao, Y., Shou, L., Chen, K., Chen, G. (2011). Aggregate Farthest-Neighbor Queries over Spatial Data. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20152-3_12
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DOI: https://doi.org/10.1007/978-3-642-20152-3_12
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