Influence constraint based Top-k spatial keyword preference query

The traditional Top-k spatial keyword preference query processing mode usually selects the range and nearest neighbor as the spatial constraints. It focuses on the influence of the distance between a spatial object and a feature object on the query result. However, the distance between feature objects and the query results and the influence of textual relevance between the feature objects and the query keywords is crucial to query results. Therefore, we propose a Threshold Inverted File Algorithm (TAIFA) to improve the query, which based on influence constraints. In order to filter out irrelevant feature objects of query keywords, TAIFA uses inverted files to store feature objects. For another, the cost of query processing is reduced by setting the upper limit score for each node of R*-tree. In return, response speed is improved greatly. And the query results are more relevant to the consumer needs. Further, the performance of TAIFA is evaluated through analysis and experimental verification. The experimental results demonstrate that the response time of the query is an order of magnitude faster than the related algorithms by pruning the irrelevant spatial objects.