With the rapid development of intelligent transportation and geographic information system, spatial data query technology has attracted the attention of many scholars. Among them, the reverse farthest neighbor query from the data point to find the target point as its farthest adjacent data points, used to obtain target set of weak influence. Its research results have been widely applied to facility location, earthquake relief, marketing and other major areas. Thus, the research of reverse furthest neighbor query technology is of great significance. However, the existing methods only deal with a single query point, and do not consider how to obtain the optimal farthest neighbor position of group reverse when the number of query points changes from one to a group. In addition, considering it is difficult to avoid some geographical location restrictions in the real situation, the existing studies are limited to road network or Euclidean space simplely, without taking the existence of obstacles into consideration. To this end, this paper proposed the V-OGRkFN(Voronoi-Obstacle Group Reverse k Farthest Neighbor) algorithm. Firstly, the algorithm gets the minimum cover circle of query points which are considered into a whole. Secondly, we use the framework based on the filtering and refining process of query by pruning strategy based on Voronoi diagram's properties. Then we get the candidate set using the theorem of transformation between k nearest neighbors and k farthest neighbors. Finally, the refining algorithm is given to get the final results. V-OGRkFN algorithm shows great performance of reverse k farthest neighbor query through the specific comparative experimental analysis. INDEX TERMS Obstacle space, reverse farthest neighbor query, Voronoi diagram, minimum coverage circle.
With the widespread application of Geographic Information System (GIS), three-dimensional spatial data, as the reflection of the real world entity, has an increasing amount of data, and the phenomenon of uneven data distribution appears. If a single spatial index structure is used to store and manage these data, there will be a waste of storage space and low query efficiency. A hybrid index structure based on 3D multi-level adaptive grid and R+ tree was proposed to solve these problems. The index structure was mainly composed of two structures, multi-level grid and R+ tree. Firstly, the data set was processed by the multi-level automatic grid algorithm based on normal distribution, and the length, width and height of the grid were obtained. Secondly, a multi-level adaptive grid structure was used to partition the data space quickly and effectively, and the advantage of zero overlap of the intermediate nodes of the R+ tree was used for efficient indexing. Finally, the maintenance and query algorithms of the index structure were given in detail, which solved the problem of low index establishment and retrieval efficiency under the condition of uneven distribution of massive data sets. In this paper, a data set subject to Gauss distribution was used to simulate the distribution of three-dimensional data. Through a large number of experimental comparison tests, it was proved that the hybrid index structure based on 3D multi-level adaptive grid-R+ tree proposed in this paper had good performance in both index structure construction and query in the case of massive data sets or uneven data distribution.
Skyline query, as a query method to solve typical multiobjective optimization problems, has a wide range of applications in market analysis and data mining. Many scholars’ attention has been attracted since it was proposed. However, the correct result set cannot be obtained easily by traditional skyline query when nonspatial and spatial attributes of the data set need to be considered at the same time, and there are differences in the importance of each attribute. To solve this problem, a skyline-like query was proposed in three-dimensional obstacle space based on the traditional skyline query. In the skyline-like query algorithm, nonspatial skyline-like points were obtained according to the traditional algorithm. The spatial attribute dominated region of the obtained points was used to filter the data set, and then the shielding of obstacles was considered in the three-dimensional obstacle space. By constructing a three-dimensional visible graph, the Dijkstra algorithm was used to obtain the skyline-like points of spatial attribute. After sorting, the skyline-like point set was obtained based on the value of user’s preference. Compared with B2S2 algorithm, the experimental results show that the skyline-like algorithm had a better performance. Then, the comparative experiments within three-dimensional obstacle skyline query were carried out by setting different sizes of data sets and different numbers of obstacles. According to the results, it is shown that the algorithm had a great performance.
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