This paper presents a nearest base-neighbor (NBN) search that can be applied to a clustered nearest neighbor problem on spatial datasets with static properties. Given two sets of data points R and S, a query point q, distance threshold δ and cardinality threshold k, the NBN query retrieves a nearest point r (called the base-point) in R where more than k points in S are located within the distance δ. In this paper, we formally define a base-point and NBN problem. As the brute-force approach to this problem in massive datasets has large computational and I/O costs, we propose in-memory and external memory processing techniques for NBN queries. In particular, our proposed in-memory algorithms are used to minimize I/Os in the external memory algorithms. Furthermore, we devise a solution-based index, which we call the neighborhood-augmented grid, to dramatically reduce the search space. A performance study is conducted both on synthetic and real datasets. Our experimental results show the efficiency of our proposed approach.
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This work was supported by the National Research Foundation of Korea (NRF) Grant Funded by the Korean Government (MSIP) (NRF-2016R1A2B1014013) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) Funded by the Ministry of Education (2016R1D1A1B03930907)
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A. Construction of grid
Algorithm 8 shows the construction procedure of the grid used in DPA. The algorithm takes two data sets (RS and SS) and δ as input. We initialize variables for grid construction and calculate the number of cells (Lines 1–6). We initialize each cell ci iteratively by the number of cells and put ci with the key value i into the grid map (Lines 7–13). In the example of Fig. 17, the key value of c10 is 10. Finally, we find the cell c where the point is located for each data point of RS and SS through Algorithm 9 and update the SCR(c) or MCR(c) or c.SS (Lines 14–20). Figure 17 shows an example of a grid construction. nCols is 5, nRows is 5 and the number of cells is 25. RN(c13) represents 21 gray cells including c13.
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Jang, H., Hyun, K., Chung, J. et al. Nearest base-neighbor search on spatial datasets. Knowl Inf Syst 62, 867–897 (2020). https://doi.org/10.1007/s10115-019-01360-3
- Information technology
- k-nearest neighbor query
- Group version of nearest neighbor query
- Nearest base-neighbor query
- Spatial databases