Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that overlap a given search region). More

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

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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...

Spatial joins are one of the most importaot operations for combining spatial objects of several relations. IO this paper, spatial join processing is studied in detail for extended spatial objects in two-dimensional 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 foliowing 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 remaioing candidates has to be tested against the join predicate. The time required for computing spatial joio predicates can essentially be reduced when objects are adequately organized in main memory. IO our approach, objects are fiist 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.

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.

The hash-join paradigm works well for relational joins, but is hard to apply to spatial joins. Relational hash-joins can guarantee that items in different hash buckets are irrelevant to each other for the purpose of join, but complexities intrinsic to spatial join predicates preclude such guarantees. It is also difficult to design spatial partition functions that produce equal-sized buckets. We examine how to apply the hash-join paradigm to spatial joins, and define a new framework for spatial hash-joins. Our spatial partition functions have two components: a set of bucket extents and an assignment function, which may map a data item into multiple buckets. Furthermore, the partition functions for the two input datasets may be different. We have designed and tested a spatial hashjoin method based on this framework. The partition function for the inner dataset is initialized by sampling the dataset, and evolves as data are inserted. The partition function for the outer dataset is immutab...

R-tree based spatial join is useful because of both its superior performance and the wide spread implementation of R-trees. We present a new R-tree join method called BFRJ (Breadth-First R-tree Join). BFRJ synchronously traverses both R-trees in breadthfirst order while processing join computation one level at a time. At each level, BFRJ creates an intermediate join index and deploys global optimization strategies (ordering, memory management, buffer management) to improve the join computation at the next level. We also present an experimental evaluation of the proposed optimizations as well as a performance comparison between BFRJ and the state-of-the-art approach. Our experimental results indicate that BFRJ with global optimizations can outperform the competitor by a significant margin (up to 50%). 0.05in This work was supported in part by the University of Michigan ITS Research Center of Excellence grant (DTFH61-93-X00017 -Sub) sponsored by the U.S. Dept. of Transportation and by...

Abstract—Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, we propose a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that, when compared with existing clustering methods, CLARANS is very efficient and effective. Second, we investigate how CLARANS can handle not only points objects, but also polygon objects efficiently. One of the methods considered, called the IR-approximation, is very efficient in clustering convex and nonconvex polygon objects. Third, building on top of CLARANS, we develop two spatial data mining algorithms that aim to discover relationships between spatial and nonspatial attributes. Both algorithms can discover knowledge that is difficult to find with existing spatial data mining algorithms. Index Terms—Spatial data mining, clustering algorithms, randomized search, computational geometry. æ 1

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.