Efficient processing of spatial joins using R-trees

Brinkhoff, Thomas; Kriegel, Hans‐Peter; Seeger, Bernhard

doi:10.1145/170035.170075

Cited by 387 publications

(194 citation statements)

References 14 publications

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“…Existing work can be categorized into space-or disk-oriented partitioning approaches. Besides advantages, both classes also have clear disadvantages: space-oriented approaches [10,11] generally need to replicate elements (elements that intersect with two partitions are copied to both) leading to considerable overhead and multiple detection of the same intersections whereas data-oriented approaches [8] suffer from the overlap problem of R-Trees which degrades performance considerably, particularly when used with dense datasets.…”

Section: Challengementioning

confidence: 99%

“…The design of our new approach considerably departs from the state of the art and avoids the overlap problem of dataoriented approaches [8,9] as well as the replication problem of space-oriented approaches [10,11]. Replication has to be avoided because it (a) increases the memory footprint, (b) requires multiple comparisons and (c) removal of duplicate results.…”

Section: Introductionmentioning

confidence: 99%

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Computational Neuroscience Breakthroughs through Innovative Data Management

Tauheed

Nobari

Biveinis

et al. 2013

Advances in Databases and Information Systems

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Abstract. Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine-grained models (including subcellular details, e.g., neurotransmitter) of the neocortex to understand the mechanisms causing brain diseases and to test new treatments in-silico. The sheer size and, more importantly, the level of detail of their models challenges today's spatial data management techniques. In collaboration with the Blue Brain project (BBP) we develop new approaches that efficiently enable analysis, navigation and discovery in spatial models of the brain. More precisely, we develop an index for the scalable and efficient execution of spatial range queries supporting model building and analysis. Furthermore, we enable navigational access to the brain models, i.e., the execution of of series of range queries where he location of each query depends on the previous ones. To efficiently support navigational access, we develop a method that uses previous query results to prefetch spatial data with high accuracy and therefore speeds up navigation. Finally, to enable discovery based on the range queries, we conceive a novel in-memory spatial join. The methods we develop considerably outperform the state of the art, but more importantly, they enable the neuroscientists to scale to building, simulating and analyzing massively bigger and more detailed brain models.

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Section: Challengementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Neuroscience Breakthroughs through Innovative Data Management

Tauheed

Nobari

Biveinis

et al. 2013

Advances in Databases and Information Systems

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“…The most common technique is the R-tree Spatial Join (RSJ) [8] which processes R-tree like index structures built on both relations R and S. RSJ is based on the lower bounding property which means that the distance between two points is never smaller than the distance (the so-called mindist, cf. figure 2) between the regions of the two pages in which the points are stored.…”

Section: Distance Range Based Similarity Joinmentioning

confidence: 99%

Supporting KDD Applications by the k-Nearest Neighbor Join

Böhm¹,

Krebs²

2003

Lecture Notes in Computer Science

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Abstract. The similarity join has become an important database primitive to support similarity search and data mining. A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects. Well-known are two types of the similarity join, the distance range join where the user defines a distance threshold for the join, and the closest point query or k-distance join which retrieves the k most similar pairs. In this paper, we propose an important, third similarity join operation called k-nearest neighbor join which combines each point of one point set with its k nearest neighbors in the other set. We discover that many standard algorithms of Knowledge Discovery in Databases (KDD) such as k-means and k-medoid clustering, nearest neighbor classification, data cleansing, postprocessing of sampling-based data mining etc. can be implemented on top of the k-nn join operation to achieve performance improvements without affecting the quality of the result of these algorithms. Our list of possible applications includes standard methods for all stages of the KDD process including preprocessing, data mining, and postprocessing. Thus, our method is turbo charging the complete KDD process.

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“…The most common technique is the R-tree Spatial Join (RSJ) [7], which is based on R-tree like index structures built on R and S. RSJ is based on the lower bounding property which means that the distance between two points is never smaller than the distance between the regions of the two pages in which the points are stored. The RSJ algorithm traverses the indexes of R and S synchronously.…”

Section: Join Algorithms Using R-treesmentioning

confidence: 99%

“…Compared to conventional index join algorithms which traverse the indexes depth-first [7] or breadth-first [14], our new algorithm improves the performance with respect to CPU and I/O. The I/O effort is reduced by two ideas: The first idea is to use more cache for the point set R which is scanned in the outermost loop.…”

Section: Join Processingmentioning

confidence: 99%