Iterative spatial join Edwin H. Jacox and Hanan Samet   View Paper (PDF)   Return to September 2003, Volume 28, Number 3 Abstract The key issue in performing spatial joins is finding the pairs of intersecting rectangles. For unin- dexed data sets, this is usually resolved by partitioning the data and then performing a plane sweep on the individual partitions. The resulting join can be viewed as a two-step process where the partition corresponds to a hash-based join while the plane-sweep corresponds to a sort-merge join. In this article, we look at extending the idea of the sort-merge join for one-dimensional data to multiple dimensions and introduce the Iterative Spatial Join. As with the sort-merge join, the Iterative Spatial Join is best suited to cases where the data is already sorted. However, as we show in the experiments, the Iterative Spatial Join performs well when internal memory is lim- ited, compared to the partitioning methods. This suggests that the Iterative Spatial Join would be useful for very large data sets or in situations where internal memory is a shared resource and is therefore limited, such as with today's database engines which share internal memory amongst several queries. Furthermore, the performance of the Iterative Spatial Join is predictable and has no parameters which need to be tuned, unlike other algorithms. The Iterative Spatial Join is based on a plane sweep algorithm, which requires the entire data set to fit in internal memory. When internal memory overflows, the Iterative Spatial Join simply makes additional passes on the data, thereby exhibiting only a gradual performance degradation. To demonstrate the use and efficacy of the Iterative Spatial Join, we first examine and analyze current approaches to performing spatial joins, and then give a detailed analysis of the Iterative Spatial Join as well as present the results of extensive testing of the algorithm, including a comparison with partitioning-based spatial join methods. These tests show that the Iterative Spatial Join overcomes the performance limitations of the other algorithms for data sets of all sizes as well as differing amounts of internal memory. ©2004 Association for Computing Machinery