Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so-called shared virtual memory which is well-suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execu-tion. In order to reduce CPU- and I/O-cost, the three phases are processed in a fashion that pre-serves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance compar-ison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed-up under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems 1

The basic concept for processing spatial joins consists of two steps: First, the spatial join is performed on the minimum bounding rectangles of the objects by using a spatial access method. This step provides a set of candidates which consists of answers (hits) and non-answers (false hits). In the second step, the exact geometry of the candidates is transferred from secondary storage into main memory and is tested against the join predicate. This step is called refinement step. It causes the main cost for computing a spatial join. In this paper, we introduce an additional filter step in order to reduce the cost of the refinement step. In this filter step more sophisticated approximations are used to identify hits as well as to filter out false hits from the set of candidates. For this purpose, we investigate various types of conservative and progressive approximations. The performance of the approximation approach is evaluated with data sets from real cartographic applications. The results show that this approach considerably reduces the total execution time of the spatial join.

For a spatial database management system, it is an important goal to minimize the I/O-cost of queries and other operations. Several page-replacement strategies have been proposed and compared for standard database systems. In the context of spatial database systems, however, the impact of buffing techniques has not been considered in detail, yet. In this paper, different page-replacement algorithms are compared for performing spatial queries. This study includes well-known techniques like LRU and LRU-K as well as new algorithms observing spatial optimization criteria. Experiments show that spatial pagereplacement algorithms outperform LRU buffers for many distributions, but not for all investigated query sets. Therefore, a combination of spatial page-replacement strategies with LRU strategies is proposed and experimentally investigated.