Abstract:

The operation of spatial data objects is frequently involved in many applications with mass and high dimensional data sets, such as spatial information and GIS. The traditional index has disadvantages with too much memory and I/O consumption in handling this kind of queries. This paper presents the PQRtree and cost model for spatial queries using PQRtree. With the improvement of the select query cost model, the corresponding query and cost model are given based on PQRtree. In order to improve the performance of the query of spatial data, a threephase parallel query method is presented. And the optimization is executed respectively in the task creation, the distribution, and the execution stages. The effective filtering and refining algorithm can improve the performance in task creation and task distribution which are applied to spatial query. The experiment shows that the model has a degree of accuracy, and the above methods  can reduce the cost of time and spatial requirements with different data sets. Given that parallel mechanism treats targets efficiently, this paper proposes a parallel mechanism cost model by using parallel query methods.

Key words: quad-tree;Rtree;query cost model;parallel mechanism;refinement