Spatio-temporal Database Based On The Grid Index Reverse Nearest Neighbor Query Technology

Developments in the field of geographic information systems, mobile computing, medical, computer-aided design and satellite image data processing, spatial data query has aroused great attention, a large number of moving objects in spatio-temporal database query algorithm effectively become increasingly important. Currently, the spatial and temporal database application development process, researchers have found that spatial data is increasing with increasing data structures are complex, expensive operation, so the spatial query is bound to the difficulties and the breakthrough point of the spatio-temporal database applications. Reverse nearest neighbor query as a new query methods, a lot of attention and application.This article studies the traditional reverse nearest neighbor search algorithm; such algorithms are based on variants of R-tree or R tree index structure to organize data. Found in practical applications, this type of query algorithms can answer reverse nearest neighbor queries, but the slow response in dealing with data sets of large-scale moving objects, can not meet the needs of users, and continuous multi-user query can not receive timely treatment, the data frequently updated, and no algorithm can promptly respond. In real life, the use of large-scale data set of reverse nearest neighbor queries often exist, so research is very important for large-scale data sets of reverse nearest neighbor query performance. Based on this, specific research Reverse nearest neighbor query processing techniques for moving objects and continuous reverse nearest neighbor query processing techniques.Static environment, a comprehensive analysis of the reverse nearest neighbor query algorithm, in-depth analysis of the reasons for the query performance when dealing with large data sets.Paradigm applied to the grid indexing mechanism to organize the moving objects, the grid-based index of the reverse nearest neighbor query processing techniques, the GI-RNN query algorithm. The algorithm to determine whether the grid cell is a candidate unit, the query space is reduced to a candidate unit set, effectively reducing the search space of query, reverse nearest neighbor query efficiency through the grid reverse nearest neighbor search of the index theorem.Continuous reverse nearest neighbor queries in a dynamic environment, specific studies on the grid-based index of the continuous reverse nearest neighbor query processing techniques, a grid-based index for reverse nearest neighbor query processing framework, and the GI-CRNN query algorithms. With the change of position of moving objects, the algorithm can update the query results, and has a good query performance and update performance.