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.

Modern database applications including computer-aided design (CAD), medical imaging, molecular biology, or Multimedia Information Systems impose new requirements on efficient spatial query processing. One of the most common query types in Spatial Database Management Systems is the spatial join. In this paper, we investigate spatial join processing for two sets of very complex spatial objects. We present an approach that is based on a fast filter step performing the spatial join on simple primitives which conservatively approximate the objects. Our main attention is focused on the problem how to generate approximations adequate for high-resolution objects. In this paper, we introduce gray approximations as a general concept which helps to range between replicating and non-replicating object approximations. The key idea of our approach is to build these replications based on statistical information taking the data distribution of the respective join-partner relation into account. Furthermore, our approach uses compression techniques for the effective storage and retrieval of the decomposed spatial objects. We demonstrate the benefits of our new method for the spatial intersection join on high resolution data. The experimental evaluation on real-world test data points out that our new concept accelerates the spatial intersection join considerably. 1.

Abstract. When viewing present-day technical applications that rely on the use of database systems, one notices that new techniques must be integrated in database management systems to be able to support these applications efficiently. This paper discusses one of these techniques in the context of supporting a Geographic Information System. It is known that the use of filters on geometric objects has a significant impact on the processing of 2-way spatial join queries. For this purpose, filters require approximations of objects. Queries can be optimized by filtering data not with just one but with several filters. Existing join methods are based on a combination of filters and a spatial index. The index is used to reduce the cost of the filter step and to minimize the cost of retrieving geometric objects from disk. In this paper we examine n-way spatial joins. Complex n-way spatial join queries require solving several 2-way joins of intermediate results. In this case, not only the profit gained from using both filters and spatial indices but also the additional cost due to using these techniques are examined. For 2-way joins of base relations these costs are considered part of physical database design. We focus on the criteria for mutually comparing filters and not on those for spatial indices. Important aspects of a multi-step filter-based n-way spatial join method are described together with performance experiments. The winning join method uses several filters with approximations that are constructed by rotating two parallel lines around the object. 1