A graph-based multilevel partitioning scheme for reducing disk access cost of spatial join processing

Spatial join queries usually access a large number of spatial data. The disk access cost of spatial join processing could be very high due to the large sizes of spatial data and the large number of spatial objects involved. A graph based multilevel data partitioning approach is proposed to partition objects into clusters for spatial join processing. Whenever the number of objects involved in a spatial join operation is greater than a threshold, say a hundred, the objects will be partitioned through a multilevel scheme, i.e., first coarsening, then partitioning, and finally uncoarsening back to the original object sets, which can be further partitioned using the known partitioning methods. The objects in a cluster are fetched together into memory and processed in a batch. Experiments have been conducted and the results have shown that our method can save 20-35% of disk access cost compared with the cases where no clustering or a little clustering is done.