12 research outputs found
Efficient Processing of Spatial Joins Using R-Trees
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
A Unified Approach for Indexed and Non-Indexed Spatial Joins
The original publication is available at www.springerlink.comL. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, J. Vahrenhold, and J. S. Vitter. âA Unified Approach for Indexed and Non-Indexed Spatial Joins,â Proceedings of the 7th International Conference on Extending Database Technology (EDBT â00), Konstanz, Germany, March 2000, published in Lecture Notes in Computer Science, Springer, 1777, Berlin, Germany, 413â429
Symbolic Intersect Detection: A Method for Improving Spatial Intersect Joins
Due to the increasing popularity of spatial databases, researchers have focused their efforts on improving the query processing performance of the most expensive spatial database operation: the spatial join. While most previous work focused on optimizing the filter step, it has been discovered recently that, for typical GIS data sets, the refinement step of spatial join processing actually requires a longer processing time than the filter step. Furthermore, two-thirds of the time in processing the refinement step is devoted to the computation of polygon intersections. To address this issue, we therefore introduce a novel approach to spatial join optimization that drastically reduces the time of the refinement step. We propose a new approach called Symbolic Intersect Detection (SID) for early detection of true hits. Our SID optimization eliminates most of the expensive polygon intersect computations required by a spatial join by exploiting the symbolic topological relationships between the two candidate polygons and their overlapping minimum bounding rectangle. One important feature of our SID optimization is that it is complementary to the state-of-the-art methods in spatial join processing and therefore can be utilized by these techniques to further optimize their performance. In this paper, we also develop an analytical cost model that characterizes SIDâs effectiveness under various conditions. Based on real map data, we furthermore conduct an experimental evaluation comparing the performance of the spatial joins with SID against the state-of-the-art approach. Our experimental results show that SID can effectively identify more than 80% of the true hits with negligible overhead. Consequently, with SID, the time needed for resolving polygon intersect in the refinement step is improved by over 50% over known techniques, as predicted by our analytical model.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45600/1/10707_2004_Article_167261.pd
A Survey on Spatial Indexing
Spatial information processing has been a centre of attention of research in the previous decade. In spatial databases, data related with spatial coordinates and extents are retrieved based on spatial proximity. A large number of spatial indexes have been proposed to make ease of efficient indexing of spatial objects in large databases and spatial data retrieval. The goal of this paper is to review the advance techniques of the access methods. This paper tries to classify the existing multidimensional access methods, according to the types of indexing, and their performance over spatial queries. K-d trees out performs quad tress without requiring additional memory usage
Multi-Dimensional Joins
We present three novel algorithms for performing multi-dimensional
joins and an in-depth survey and analysis of a low-dimensional
spatial join. The first algorithm, the Iterative Spatial Join,
performs a spatial join on low-dimensional data and is based
on a plane-sweep technique.
As we show analytically and experimentally,
the Iterative Spatial Join performs well when internal memory is
limited, compared to competing methods. This suggests that
the Iterative Spatial Join would be useful for very large data sets
or in situations where internal memory is a shared resource and
is therefore limited, such as with today's database engines which
share internal memory amongst several queries. Furthermore, the
performance of the Iterative Spatial Join is predictable and has
no parameters which need to be tuned, unlike other algorithms.
The second algorithm, the Quickjoin algorithm,
performs a higher-dimensional
similarity join in which pairs of objects that lie within a
certain distance epsilon of each other are reported.
The Quickjoin algorithm overcomes drawbacks of competing methods,
such as requiring embedding methods on the data first or using
multi-dimensional indices, which limit
the ability to discriminate between objects in each
dimension, thereby degrading performance.
A formal analysis is provided of the Quickjoin method, and
experiments show that the Quickjoin method significantly outperforms
competing methods.
The third algorithm adapts
incremental join techniques to improve the
speed of calculating the Hausdorff distance, which
is used in applications such as image matching, image analysis,
and surface approximations.
The nearest neighbor incremental join technique for indices that
are based on hierarchical containment use a priority queue
of index node pairs and bounds on the distance values between
pairs, both of which need to modified in order to calculate the
Hausdorff distance. Results of experiments are described that
confirm the performance improvement.
Finally, a survey is provided which
instead of just summarizing the literature and presenting each
technique in its entirety, describes distinct components of
the different techniques, and each technique is decomposed into
an overall framework for performing a spatial join
DISTANCE-ASSOCIATED JOIN INDICES FOR SPATIAL RANGE SEARCH
Spatial join indices are join indices constructed for spatial objects. Similar to join indices for relational data-base systems, spatial join indices improve efficiency of spatial join operations. In this paper, a distance-associated join index structure is developed to speed up spatial queries especially for spatial range queries. Three distance-associated join indexing mechanisms: basic, ring-structured and hierarchical, are presented and stu-died. Our analysis and performance study shows that distance-associated spatial join indices substantially improve the performance of spatial queries, and different structures are best suited for different applications. 1