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 not found

This paper describes PBSM (Partition Based Spatial--Merge), a new algorithm for performing spatial join operation. This algorithm is especially effective when neither of the inputs to the join have an index on the joining attribute. Such a situation could arise if both inputs to the join are intermediate results in a complex query, or in a parallel environment where the inputs must be dynamically redistributed. The PBSM algorithm partitions the inputs into manageable chunks, and joins them using a computational geometry based plane--sweeping technique. This paper also presents a performance study comparing the the traditional indexed nested loops join algorithm, a spatial join algorithm based on joining spatial indices, and the PBSM algorithm. These comparisons are based on complete implementations of these algorithms in Paradise, a database system for handling GIS applications. Using real data sets, the performance study examines the behavior of these spatial join algorithms in a vari...

Two new spatial join operations, distance join and distance semijoin, are introduced where the join output is ordered by the distance between the spatial attribute values of the joined tuples. Incremental algorithms are presented for computing these operations, which can be used in a pipelined fashion, thereby obviating the need to wait for their completion when only a few tuples are needed. The algorithms can be used with a large class of hierarchical spatial data structures and arbitrary spatial data types in any dimensions. In addition, any distance metric may be employed. A performance study using Rtrees shows that the incremental algorithms outperform non-incremental approaches by an order of magnitude if only a small part of the result is needed, while the penalty, if any, for the incremental processing is modest if the entire join result is required.

In this paper, we consider the filter step of the spatial join problem, for the case where neither of the inputs are indexed. We present a new algorithm, Scalable Sweeping-Based Spatial Join (SSSJ), that achieves both efficiency on real-life data and robustness against highly skewed and worst-case data sets. The algorithm combines a method with theoretically optimal bounds on I/O transfers based on the recently proposed distribution-sweeping technique with a highly optimized implementation of internal-memory plane-sweeping. We present experimental results based on an efficient implementation of the SSSJ algorithm, and compare it to the state-ofthe-art Partition-Based Spatial-Merge (PBSM) algorithm of Pate1 and DeWitt.

Performing a fair comparison of the various spatial join techniques that have been proposed in the past decade is a challenging task, since they have been validated through experimental evaluation with no common methodology, on different platforms, with various datasets and implementation choices. It is then even more difficult to provide guidelines for generating optimal plans for complex spatial queries involving several spatial joins (multi-way joins). The objective of this paper is two fold: (i) to propose a common framework and evaluation platform for spatial query processing, and (ii) to use it to experimentally evaluate spatial join processing strategies. We provide a first evaluation of query execution plans (QEP) in the case of queries with one or two spatial joins. The QEPs assume R -tree indexed relations and use a common set of spatial joins algorithms, among which one is a novel extension of a strategy based on an on-the-fly index creation prior to the join with another...

: The join query is one of the fundamental operations in Data Base Management Systems (DBMSs). Modern DBMSs should be able to support non-traditional data, including spatial objects, in an efficient manner. Towards this goal, spatial data structures can be adopted in order to support the execution of join queries on sets of multidimensional data. This paper introduces analytical models that estimate the cost (in terms of node or disk accesses) of join queries involving two multidimensional indexed data sets using R-tree-based structures. In addition, experimental results are presented, which show the accuracy of the analytical estimations when compared to actual runs on both synthetic and real data sets. It turns out that the relative error rarely exceeds 15% for all combinations, a fact that makes the proposed cost models useful tools for efficient spatial query optimization. 1. Introduction A Spatial Data Base Management System (SDBMS) should offer appropriate data types and query ...

Spatial joins are join operations that involve spatial data types and operators. Spatial access methods are often used to speed up the computation of spatial joins. This paper addresses the issue of benchmarking spatial join operations. For this purpose, we first present a WWW-based tool to produce sets of rectangles. Experimentators can use a standard Web browser to specify the number of rectangles, as well as the statistical distributions of their sizes, shapes, and locations. Second, using the rectangle generator and a well-defined set of statistical models we defined several test suites to compare the performance of three spatial join algorithms: nested loop, scan-and-index, and synchronized tree traversal. We also added a reallife data set from the Sequoia 2000 storage benchmark. Our results confirm that the use of spatial indices leads to performance gains of several orders of magnitude. The tests also show that highly selective join predicates enjoy greater performanc...

. Most spatial join algorithms either assume the existence of a spatial index structure that is traversed during the join process, or solve the problem by sorting, partitioning, or on-the-fly index construction. In this paper, we develop a simple plane-sweeping algorithm that unifies the index-based and non-index based approaches. This algorithm processes indexed as well as non-indexed inputs, extends naturally to multi-way joins, and can be built easily from a few standard operations. We present the results of a comparative study of the new algorithm with several index-based and non-index based spatial join algorithms. We consider a number of factors, including the relative performance of CPU and disk, the quality of the spatial indexes, and the sizes of the input relations. An important conclusion from our work is that using an index-based approach whenever indexes are available does not always lead to the best execution time, and hence we propose the use of a simple cost...

In recent years, many access structures have been proposed supporting access to objects via their spatial location. However, additional non-geometric properties are always associated with geometric objects, and in practice it is often necessary to use select conditions based on spatial and standard attributes. An obvious idea to improve the performance of queries with mixed select conditions is to extend spatial access structures with additional dimensions for standard attributes. Whereas this idea seems to be simple and promising at rst glance, a closer look brings up serious problems, especially with select conditions containing arithmetic expressions or select conditions for non-point objects and with Boolean operators like or and not. In this paper we present a solution to overcome the problems sketched above which is based on three pillars: (1) We present powerful basic techniques to deal with arithmetic conditions containing mathematical operations (like `+', `;', ` ', and `=') and range queries for non-point objects. (2) We introduce a technique which allows to decompose select conditions containing Boolean operators and to reduce the processing of such a select condition to the processing of its elementary parts. (3) We showhow other operations like joins and distance-scans can be integrated into this query processing architecture.