Research On Moving Objects And Its Nearest Neighbor Query Algorithms

With the rapid development of mobile computing, wireless communication, and positioning technologies, the storage and management of a variety of spatial and spatiotemporal objects emerges, which also makes tracking and recording the position of moving objects possible. Many applications areas (such as geographical information systems, smart navigation systems, traffic control, weather forecast, digital battlefield, mobile e-commerce, etc.) call for searching such data objects eminently. Therefore, the problem of spatial data queries quickly became a hot research, nearest neighbor queries of moving objects for spatial data query is one of the most important inquiry, it concerns a wide range in all areas of society.The query efficiency is one of the most important benchmarks of the performance of nearest neighbor queries for moving objects, although numerous researchers have been strived on this area and obtained a number of valuable results. However, the current algorithms are mainly focused on how to improve the efficiency of a single query, but in practical application, the server will often receive a large number of queries, using the traditional nearest neighbor query algorithm in dealing with such situations and less efficient.Motivated by this, in this paper, we analyzed and studied the traditional network-based nearest neighbor queries of moving objects algorithm, and proposed a nearest neighbor query algorithm which was based on cluster query points. The main contribution of this paper can be summarized as:1. We expanded and improved the Jensen model, and also gave a complete network and object model, for the nearest neighbor query algorithm of moving objects research provide a realistic network environment.2. We improved the FNR-tree based on road networks, and proposed the FNR~*-tree. For the nearest neighbor query algorithm of moving objects research provide an efficient support, reducing the retrieval time of moving objects.3. We proposed the nearest neighbor query algorithm C_NN based on clustering of moving objects. For the rapid changes in the characteristics of moving objects, C_NN cluster the query point and calculate the results. To some extent, it solved the problem of low efficiency when the traditional network-based nearest neighbor query algorithm of moving objects deal with more query point.4. By using the data sets generator generated moving objects, we take a comparison of C_NN algorithm with the traditional algorithm and FNR and FNR*-tree. Experimental results show that the proposed C_NN outperforms the traditional algorithm, the search efficiency of FNR*-tree is higher than FNR tree.