Improving Performance with Bulk-Inserts in Oracle R-Trees

An, Ning; Kanth, Ravi; Kothuri, V.; Ravada, Siva

doi:10.1016/b978-012722442-8/50088-4

Cited by 16 publications

(8 citation statements)

References 7 publications

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“…In [1], a new Oracle's approach for performing bulk-insertion in R-trees is presented. The characteristics of this approach are: (1) batched insertion into subtrees resulting in fast insertion times, and (2) fast reorganization of subtrees whenever there is an overlap, to ensure good quality of the final R-tree.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…The main target of the bulk-insertion process is to create a good enough spatial index in order to reduce the loading time, the query cost of the resulting index structure, or both. In [1] the bulkinsertion techniques are roughly classified into two categories: merge-based and buffer-based techniques.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…-The buffer-based bulk-insertion techniques use the buffer-tree [2] as buffering technique for the bulk-insertion process. Most of the bulk-insertion methods belong to the first category and concern the R-tree [8,15,3,5,4,1].…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Bulk Insertions into xBR $$^{+}$$ -trees

Roumelis

Vassilakopoulos

Corral

et al. 2017

Model and Data Engineering

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Abstract. Bulk insertion refers to the process of updating an existing index by inserting a large batch of new data, treating the items of this batch as a whole and not by inserting these items one-by-one. Bulk insertion is related to bulk loading, which refers to the process of creating a nonexisting index from scratch, when the dataset to be indexed is available beforehand. The xBR + -tree is a balanced, disk-resident, Quadtree-based index for point data, which is very efficient for processing spatial queries. In this paper, we present the first algorithm for bulk insertion into xBR + -trees. This algorithm incorporates extensions of techniques that we have recently developed for bulk loading xBR + -trees. Moreover, using real and artificial datasets of various cardinalities, we present an experimental comparison of this algorithm vs. inserting items one-by-one for updating xBR + -trees, regarding performance (I/O and execution time) and the characteristics of the resulting trees. We also present experimental results regarding the query-processing efficiency of xBR + -trees built by bulk insertions vs. xBR + -trees built by inserting items one-by-one.

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Section: Related Work and Motivationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

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Bulk Insertions into xBR $$^{+}$$ -trees

Roumelis

Vassilakopoulos

Corral

et al. 2017

Model and Data Engineering

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“…The bulk-loading methods can be classified into three categories: sort-based, buffer-based and sampled-based methods [3]. The sorted-based methods are the ones most studied in the literature and they are widely used in popular spatial DBMS [5,6,7], because they roughly consist of sorting the data and create the tree in a bottom-up fashion.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for bulk-loading xBR+-trees

Roumelis

Vassilakopoulos

Corral

et al. 2018

Computer Standards & Interfaces

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A major part of the interface to a database is made up of the queries that can be addressed to this database and answered (processed) in an efficient way, contributing to the quality of the developed software. Efficiently processed spatial queries constitute a fundamental part of the interface to spatial databases due to the wide area of applications that may address such queries, like geographical information systems (GIS), location-based services, computer visualization, automated mapping, facilities management, etc. Another important capability of the interface to a spatial database is to offer the creation of efficient index structures to speed up spatial query processing. The xBR + -tree is a balanced disk-resident quadtree-based index structure for point data, which is very efficient for processing such queries. Bulkloading refers to the process of creating an index from scratch, when the dataset to be indexed is available beforehand, instead of creating the index gradually (and more slowly), when the dataset elements are inserted one-by-one. In this paper, we present an algorithm for bulk-loading xBR + -trees for big datasets residing on disk, using a limited amount of main memory. The resulting tree is not only built fast, but exhibits high performance in processing a broad range of spatial queries, where one or two datasets are involved. To justify these characteristics, using real and artificial datasets of various cardinalities, first, we present an experimental comparison of this algorithm vs. a previous version of the same algorithm and STR, a popular algorithm of bulk-loading R-trees, regarding tree creation time and the characteristics of the trees created, and second, we experimentally compare the query efficiency of bulk-loaded xBR + -trees vs. bulk-loaded R-trees, regarding I/O and execution time. Thus, this paper contributes to the implementation of spatial database interfaces and the efficient storage organization for big spatial data management.

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“…Although the R-tree has the problems listed above, some major database vendors [1] have used the R-tree to provide spatial database support for two reasons. First, most spatial data are 2D and 3D.…”

Section: Introductionmentioning

confidence: 99%

The hB-pi* Tree: An Optimized Comprehensive Access Method for Frequent-Update Multi-dimensional Point Data

Zhou¹,

Salzberg

Lecture Notes in Computer Science

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Abstract. The R-tree [7] family is the most popular multi-dimensional index method. The R-tree, however, has overlaps among index entries and its index page fanout decreases rapidly as data dimension increases. Furthermore, the R-tree has poor concurrency performance. For frequent-update multi-dimensional point data sets, the hB-pi [5] tree is a better choice than the R*-tree. But the hB-pi tree (and all other kd-tree based access methods) indexes the whole space no matter whether or not there is any data in some sub-spaces. Indexing empty space (i.e., space without data inside) leads to unnecessary data page accesses which increase with growing dimension. This paper addresses this problem by proposing the hB-pi* tree, which efficiently indicates empty spaces and improves range query performances while preserving the hB-pi's high fan-out and good concurrency. Our methods can be applied to any kd-tree based access methods, and our claims are supported by extensive experimental evaluation.

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Improving Performance with Bulk-Inserts in Oracle R-Trees

Cited by 16 publications

References 7 publications

Bulk Insertions into xBR $$^{+}$$ -trees

Bulk Insertions into xBR $$^{+}$$ -trees

An efficient algorithm for bulk-loading xBR+-trees

The hB-pi* Tree: An Optimized Comprehensive Access Method for Frequent-Update Multi-dimensional Point Data

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