STR: a simple and efficient algorithm for R-tree packing

Leutenegger, Scott T.; Lopez, Mario; Edgington, Jeffrey

doi:10.1109/icde.1997.582015

Cited by 323 publications

(263 citation statements)

References 11 publications

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“…An insertion of a new rectangle can increase the overlap and several heuristics for choosing which leaf to insert a new rectangle into, as well as for splitting nodes during rebalancing, have been proposed 9, 16, 19, 28 . Bulk loading an R-tree with N rectangles using the naive method of repeated insertion takes O N log B N I Os, which has been recognized to beabysmally slow. Several bulk loading algorithms using O N B log M = B N B I Os have been proposed 10,13,18,21,26,30 . These algorithms are more than a factor of B faster than the repeated insertion algorithm.…”

Section: Previous Results On Bulk Operations On R-treesmentioning

confidence: 99%

Efficient Bulk Operations on Dynamic R-trees

Arge

Hinrichs

Vahrenhold

et al. 1999

Algorithm Engineering and Experimentation

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In recent y ears, there has been an upsurge of interest in spatial databases. A major issue is how to e ciently manipulate massive amounts of spatial data stored on disk in multidimensional spatial indexes data structures . Construction of spatial indexes bulk loading has been studied intensively in the database community. The continuous arrival of massive amounts of new data makes it important to e ciently update existing indexes bulk updating .In this paper, we present a simple, yet e cient technique for performing bulk update and query operations on multidimensional indexes. We present our technique in terms of the so-called R-tree and its variants, as they have emerged as practically e cient indexing methods for spatial data. Our method uses ideas from the bu er tree lazy bu ering technique and fully utilizes the available internal memory and the page size of the operating system. We give a theoretical analysis of our technique, showing that it is e cient both in terms of I O communication, disk storage, and internal computation time. We also present the results of an extensive set of experiments showing that in practice our approach performs better than the previously best known bulk update methods with respect to update time, and that it produces a better quality index in terms of query performance. One important novel feature of our technique is that in most cases it allows us to perform a batch of updates and queries simultaneously. To beable to do so is essential in environments where queries have to beanswered even while the index is being updated and reorganized.

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Section: Previous Results On Bulk Operations On R-treesmentioning

confidence: 99%

Efficient Bulk Operations on Dynamic R-trees

Arge

Hinrichs

Vahrenhold

et al. 1999

Algorithm Engineering and Experimentation

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“…The process is continued recursively. Hibert Packed R-Tree [7] and STR R-Tree [11] were derived from this basic approach. Hilbert Packed R-Tree uses the Hilbert value of the centroids of the rectangles for sorting.…”

Section: Packed R-treementioning

confidence: 99%

A State-of-Art in R-Tree Variants for Spatial Indexing

Balasubramanian¹,

Sugumaran²

2012

IJCA

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Nowadays, indexing has become essential for fast retrieval of results. Spatial databases are used in many applications which demand faster retrieval of data. These data are multidimensional. Designing index structure for spatial databases is current area of research. R-Tree is the most widely used index structure for multi-dimensional data. Many variants of R-Tree has evolved with each performing better in some aspect like query retrieval, insertion cost, application specific and so on. In this work, state-of-art of variants in R-Tree is presented. This paper provides an idea of the present development in spatial indexing and paves way for the researchers to explore and analyze the difficulties and trade-offs in the work. The RTree variants are classified according to the way they are different from the original R-Tree either in the process of construction or whether it is a hybrid of R-Tree and some other structure or whether it is an extension of R-Tree to support many other applications.

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“…The gure shows an example of a region query q = <(27.53, 15.75), (30.71, 19)>. The translation of this query to the representative matrix de nes the range of valid columns [6,10] and the range of valid rows [9,14]. The algorithm to solve the query begins with the traversal of the wavelet tree.…”

Section: Solving Queriesmentioning

confidence: 99%

A New Point Access Method Based on Wavelet Trees

Brisaboa

Luaces

Navarro

et al. 2009

Lecture Notes in Computer Science

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Abstract. The development of index structures that allow e cient retrieval of spatial objects has been a topic of interest in the last decades. Most of these structures have been designed for secondary memory. However, in the last years the price of memory has decreased drastically. Nowadays it is feasible to place complete spatial indexes in main memory. In this paper we focus in a subcategory of spatial indexes named Point Access Methods. These indexes are designed to solve the problem of indexing points. We present a new index structure designed for two dimensions and main memory that keeps a good trade-o between the space needed to store the index and its search e ciency. Our structure is based on a wavelet tree, which was originally designed to represent sequences, but has been successfully used as an index in areas like information retrieval or image compression.

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STR: a simple and efficient algorithm for R-tree packing

Cited by 323 publications

References 11 publications

Efficient Bulk Operations on Dynamic R-trees

Efficient Bulk Operations on Dynamic R-trees

A State-of-Art in R-Tree Variants for Spatial Indexing

A New Point Access Method Based on Wavelet Trees

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