Combination of negative keywords spatial keyword query

Spatial keyword query oriented to personalized constraints is a hot issue in the field of database query, in which the rapidity and matching are the core issues to evaluate the merits of such query. The traditional spatial keyword range query is unable to match the query with personalized constraints except geographical location and keyword information, and the construction and update speed and query efficiency of most index structures in two-dimensional space are low. Aiming at these problems, puts forward a constraint with negative keywords (i.e., users don't like keywords) query model, USES the Geohash string representation point of interest object, built after the string sorting B+Tree as a binary Tree leaf nodes, through the binary Tree to filter object with negative keywords, to build a hybrid index structure based on Geohash BGIB-Tree. On this basis, the prefix matching search algorithm is designed based on the recursion of Geohash encoding. The pruning strategy is based on the region encoding and the object encoding prefix matching to quickly find the points of interest that meet the spatial constraints. Finally, the query can be completed by two-way search in the inverted index. By comparing with IR-Tree and BIR-Tree, the influence of BGIB-tree's construction time and related parameters on the query algorithm was verified on the real data set. The experimen tresults proved show that the index construction time was reduced by 30% and the query efficiency of the algorithm was improved by 29%.