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

Abstract: Due to the high complexity of objects and queries and also due to extremely large data volumes, geographic database systems impose stringent requirements on their storage and access architecture with respect to efficient query processing. Performance improving concepts such as spatial storage and access structures, approximations, object decompositions and multi-phase query processing have been suggested and analyzed as single building blocks. In this paper, we describe a storage and access architecture which is composed from the above building blocks in a modular fashion. Additionally, we incorporate into our architecture a new ingredient, the scene organization, for efficiently supporting set-oriented access of large-area region queries. An experimental performance comparison demonstrates that the concept of scene organization leads to considerable performance improvements for large-area region queries by a factor of up to 150. 1

Global clustering has rarely been investigated in the area of spatial dambase systems although dramatic performance improvements can be achieved by using suitable techniques. In this paper, we propose a simple approach to global clustering called cluster organization. We will demonstrate that this cluster organization leads to considerable performance improvements without any algorithmic ovedxad. Based on real geographic data, we perfm a detailed empirical performance evaluation and compare the clusterorganixation to other organization models not using global clustering. We will show that global clustering speeds up the processing of window queries as well as spatial joins without decreasing the performanceoftbeinsertionofnewobjectsaodofse, lective queries such as point queries. lhe spatial join is sped up by a factor of about 4, whereas non-selective window queries are accelerated by even higher speed up factors. 1