A join-index is a data structure used for processing join queries in databases. Join-indices use precomputation techniques to speed up online query processing and are useful for data sets which are updated infrequently. The I/O cost of join computation using a join-index with limited buffer space depends primarily on the page-access sequence used to fetch the pages of the base relations. Given a join-index, we introduce a suite of methods based on clustering to compute the joins. We derive upper bounds on the length of the page-access sequences. Experimental results with Sequoia 2000 data sets show that the clustering method outperforms existing methods based on sorting and online-clustering heuristics.

Spatial databases, addressing the growing data management and analysis needs of spatial applications such as geographic information systems, have been an active area of research for more than two decades. This research has produced a taxonomy of models for space, spatial data types and operators, spatial query languages and processing strategies, as well as spatial indexes and clustering techniques. However, more research is needed to improve support for network and field data, as well as query processing (e.g., cost models, bulk load). Another important need is to apply spatial data management accomplishments to newer applications, such as data warehouses and multimedia information systems. The objective of this paper is to identify recent accomplishments and associated research needs of the near term.

The cost of join computation, which uses a join index in a sequential system with limited buffer space, depends primarily on the page access sequence used to fetch the pages of the base relations. We introduce a graph partitioning model that will minimize the length of the page access sequence thus minimizing the redundant I/O, given a fixed buffer. Experiments with Sequoia 2000 data sets show that the graph partitioning method outperforms the existing methods based on sorting and online clustering, particularly for a small number of buffers and high join selectivity.

Declustering and load balancing are important issues in designing a high performance geographic information system (HPGIS), which is a central component of many interactive applications such as real time terrain visualization. The current literature provides efficient methods for declustering spatial point data. However, there has been little work toward developing efficient declustering methods for collections of extended objects, like chains of line segments and polygons. We focus on the data partitioning approach to parallelizing GIS operations. We provide a framework for declustering collections of extended spatial objects by identifying the following key issues: (1) work load metric; (2) spatial extent of the work load; (3) distribution of the work load over the spatial extent; and (4) declustering method. We identify and experimentally evaluate alternatives for each of these issues. In addition, we also provide a framework for dynamically balancing the load between different processors. We experimentally evaluate the proposed declustering and load balancing methods on a distributed memory MIMD machine (Cray T3D). Experimental results show that the spatial extent and the work load metric are important issues in developing a declustering method. Experiments also show that the replication of data is usually needed to facilitate dynamic load balancing, since the cost of local processing is often less than the cost of data transfer for extended spatial objects. In addition, we also show that the effectiveness of dynamic load balancing techniques can be improved by using declustering methods to determine the subsets of spatial objects to be transferred during runtime.

Abstract We experimentally evaluate sequential and distributed implementations of an approximation partitioning algorithm by Kalpakis and Sherman for the Geometric Steiner Minimum Tree Problem (GSMT) in R d for d = 2,3. Our implementations incorporate an improved method for combining the subproblems, and single‐and double‐edge reduction techniques to eliminate unnecessary Steiner points created by the partitioning process. Results show that these refinements are crucial in practice to produce high‐quality results. Moreover, with these refinements, the partitioning algorithm is an effective practical heuristic, which in less time finds solutions roughly comparable with those computed by Smith's Algorithm. The partitioning algorithm depends on a parameter t through which the user can trade off time for solution quality. To solve each of the subproblems of size at most t , we apply Smith's Algorithm, the only other Steiner tree algorithm known for R 3 under the Euclidean norm. Using uniformly generated point sets in the d ‐cube [0, 100] d , we compare the running time and solution quality for the partitioning algorithm for various values of t against that of Smith's Algorithm applied to the entire input. We also estimate the constant factor in the 1 + O(t −1/d(d‐1) ) asymptotic performance bound proven by Kalpakis and Sherman and find that it is less than one. With d = 2 on input sets of 25 points, our sequential implementation with t = 7 typically found within 6 seconds a Steiner tree within 1% of the cost (342) of that computed by Smith's Algorithm in 20 seconds. At t = 13 and using approximately 15 seconds, our sequential implementation typically found Steiner trees within 0.1% of this cost. Similarly, with d = 3 on input sets of 60 points, our distributed implementation with t = 10 typically found within 22 seconds a Steiner tree within 1% of the cost (1059) of that computed by Smith's Algorithm in 200 seconds. At t = 31 and using approximately 108 seconds, our distributed implementation usually found slightly better Steiner trees than did Smith's Algorithm. © 1994 by John Wiley & Sons, Inc.