Branch grafting method for R-tree implementation

Schreck, Thomas; Chen, Z.

doi:10.1016/s0164-1212(00)00057-1

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…As indicated in our discussion, we have taken a bottom-up approach in our research: from efficient spatial data structure (i.e., branch-grafted R tree implementation) [1,6], to efficient data access methods [3], and finally, to effective spatial data mining [5]. Since previous experiments have shown that there are significant advantages of using branchgrafted implementation, this bottom-up approach exploits the performance advantages of the branch-grafted R-trees.…”

Section: Resultsmentioning

confidence: 99%

“…Basic spatial data access methods developed in earlier work of our institution [1,3,6] include window search algorithm (also called overlap algorithm, which finds all the objects that overlap a specified search window and can be used for location-based queries) and its variation window search algorithm (also referred to as contains algorithm), search by distance algorithm and its extension nearest neighbor algorithm, etc.…”

Section: Introductionmentioning

confidence: 99%

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Using Branch-Grafted R-trees for Spatial Data Mining

Dubey

Chen

Shi

2004

Computational Science - ICCS 2004

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Abstract. Spatial data mining is a process of extraction of implicit information, such as weather patterns around latitudes, spatial features in a region, etc., with a goal of knowledge discovery. The work reported here is based on our earlier work on branch-grafted R trees. We have taken a bottom-up approach in our research: from efficient spatial data structure (i.e., branch-grafted R tree implementation), to efficient data access methods, and finally, to effective spatial data mining. Since previous experiments have shown that there are significant advantages of using branch-grafted implementation, this bottom-up approach exploits the performance advantages of the branch-grafted R-trees.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Branch-Grafted R-trees for Spatial Data Mining

Dubey

Chen

Shi

2004

Computational Science - ICCS 2004

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“…attempts to reduce coverage and overlap using a combination of a revised node split algorithm and the concept of forced reinsertion at node overflow. Hilbert R-tree [18], branch grafting method [19], and compact R-tree [20] are other variants of R-tree structure that aim to achieve better storage utilization. For recent papers and books serving as extensive surveys on R-tree related bibliography, see, among others, [14,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A new enhancement to the R-tree node splitting

Al-Badarneh

Yaseen

Hmeidi

2009

Journal of Information Science

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The performance of spatial queries depends mainly on the underlying index structure used to handle them. R-tree, a well-known spatial index structure, suffers largely from high overlap and high coverage resulting mainly from splitting the overflowed nodes. Assigning the remaining entries to the underflow node in order to meet the R-tree minimum fill constraint ( Remaining Entries problem) may induce high overlap or high coverage. This is done without considering the geometric features of the remaining entries and this may cause a very non-optimized expansion of that particular node. This paper presents a solution to the above problem. The proposed solution to this problem distributes rectangles as follows: (1) assign m entries to the first node, which are nearest to the first seed; (2) assign other m entries to the second node, which are nearest to the second seed; (3) assign the remaining entries one by one to the nearest seed. Several experiments on real data, as well as synthetic data, show that the proposed splitting algorithm outperforms the efficient version of the original R-tree in terms of query performance.

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“…The R * -tree [5] uses two kinds of optimization criteria in its insertion algorithm: the margin criterion to select a split axis and the overlap criterion to select a distribution along the split axis. Hilbert R-tree [7], branch grafting method [10], and compact R-tree [11] aim to achieve better storage utilization.…”

Section: Introductionmentioning

confidence: 99%

Linear R-Tree Revisited

Al-Badarneh¹,

Tawil²

2009

International Journal of Computers and Applications

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The problem of finding an optimal splitting of overflowed nodes has a major influence on query performance of the R-tree spatial index structure. Most of the previous split heuristics of R-tree-based index structures have quadratic time and face the problem of increasing overlap of the resulting minimum bounding rectangles (MBRs). In this paper, we propose an efficient heuristic method for handling R-tree node splits. The proposed method is an enhancement of the Linear R-tree method proposed in C. Ang & T. Tan, New linear node splitting algorithm for R-trees, Proc. 5th Int. Symposium on Advances in Spatial Databases, 1997, 339-349. However, it provides a solution to resolve the distance tie problem to prevent the splitting algorithm from halting. From a practical point of view, the new splitting method is very attractive because it has linear time complexity, acceptable space utilization, as well as better query performance. Results from several experiments conducted using real-life as well as synthetic datasets show that the proposed splitting method outperforms the quadratic version of the original R-tree in terms of query performance and construction time.

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Branch grafting method for R-tree implementation

Cited by 10 publications

References 9 publications

Using Branch-Grafted R-trees for Spatial Data Mining

Using Branch-Grafted R-trees for Spatial Data Mining

A new enhancement to the R-tree node splitting

Linear R-Tree Revisited

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