ABSTRACT Finding the nearest neighbors and finding the farthest neighbors are fundamental problems in spatial databases. Consider two sets of data points in a two-dimensional data space, which represent a set of favor locations F, such as... more
ABSTRACT Finding the nearest neighbors and finding the farthest neighbors are fundamental problems in spatial databases. Consider two sets of data points in a two-dimensional data space, which represent a set of favor locations F, such as libraries and schools, and a set of disfavor locations D, such as dumps and gambling houses. Given another set of data points C in this space as houses for rent, one who needs to rent a house may need a recommendation which takes into account the favor and disfavor locations. To solve this problem, a new two-dimensional data space is employed, in which dimension X describes the distance from a data point c in C to its nearest neighbor in D and dimension Y describes the distance from c to its farthest neighbor in F. Notice that the larger value is preferred in dimension X while the smaller value is preferred in dimension Y. Following the above dominance rule, the recommendation for the house renting can be achieved by a skyline query. A naïve method to processing this query is 1) to find the nearest neighbor from D and the farthest neighbor from F for each data point in C and then 2) to construct a new two-dimensional data space based on the results from 1) and to apply any of the existing skyline algorithms to get the answer. In this paper, based on the quad-tree index, we propose an efficient algorithm to answer this query by combining the above two steps. A series of experiments with synthetic data and real data are performed to evaluate this approach and the experiment results demonstrate the efficiency of the approach.
