Spatial joins are one of the most important operations for combining spatial objects of several relations. The efficient processing of a spatial join is extremely important since its execution time 1ssuper-Iinear in the number of spatial objects of the participating relations, arrd this number of objects may be very high. In this paper, we present a first detailed study of spatial join processing using Rtrees, particularly R*-trees. R-trees are very suitable for supporting spatial queries and the R*-tree is one of the most efficient members of the R-tree family. Starting from a straightforward approach, we present several techniques for improving its execution time with respect to both, CPU-and I/O-tirrre. Eventually, we end up with an algorithm whose total execution time is improved over the fwst approach by an order of magnitude. Using a buffer of reasonable size, I/O-time is almost optimal, i.e. it ahnost corresponds to the time for reading each required page of the relations exactly once. The performance of the various approaches 1s investigated in an experimental performance comparison where several large data sets from real applications are used.
Spatial joins are one of the most important operations for combining spatial objects of several relations. In this paper, spatial join processing is studied in detail for extended spatial objects in twodimensional data space. We present an approach for spatial join processing that is based on three steps. First, a spatial join is performed on the minimum bounding rectangles of the objects returning a set of candidates. Various approaches for accelerating this step of join processing have been examined at the last year's conference [BKS 93a]. In this paper, we focus on the problem how to compute the answers from the set of candidates which is handled by the following two steps. First of all, sophisticated approximations are used to identify answers 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. In the last step, the exact geometry of the remaining candidates has to be tested against the join predicate. The time required for computing spatial join predicates can essentially be reduced when objects are adequately organized in main memory. In our approach, objects are first decomposed into simple components which are exclusively organized by a main-memory resident spatial data structure. Overall, we present a complete approach of spatial join processing on complex spatial objects. The performance of the individual steps of our approach is evaluated with data sets from real cartographic applications. The results show that our approach reduces the total execution time of the spatial join by factors.
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