Analytical cost model for spatial joins using R-trees;

However,the exact intersection test of two spatial objects is the most timeconsuming and I/O-consuming step in processing spatial joins.

For reinforcement learning control in continuous spaces,a Q-learning method based on a self-organizing fuzzy RBF(radial basis function) network is proposed.

This paper is to study genetic algorithm in the unified framework of stochastic processes in continuous space.

To overcome the multiple redundancy and low efficient solving of genetic algorithms as a result of no feedback ability, as well as low speed of ant colony algorithms owing to absence of original pheromone, a modified ant colony algorithm has been improved, which is capable of searching in continuous space, to integrate with genetic algorithm to complement each other’s advantages.

A multi-channel FORJ,by incorporating spatial interconnection and wavelength division multiplexing(WDM)technologies,is proposed and fabricated.

Improved cost model for spatial joins using R-trees

R-tree spatial join algorithm BFGS (Breadth-First Group-based Sweeping) was introduced based on the breath-first group-based plane sweeping paradigm.

After analyzing the previous researches on the spatial join, this paper introduced a new method, orthogonal polygons, to approximate actual spatial data in spatial access methods, and to join spatial relation based on R trees.