A New Double Sorting-Based Node Splitting Algorithm for R-Tree

Коротков, А.С.

doi:10.15514/syrcose-2011-5-5

Cited by 4 publications

(2 citation statements)

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“…The double sorting-based algorithm introduced in Korotkov [25] has two versions: first is for one-dimensional (1D) splitting only, the other is for two or more dimensional splitting. Both versions use separate arrays (two per dimension) to store lower and higher values of the objects' MBRs.…”

Section: Related Workmentioning

confidence: 99%

SOLD: A node-Splitting algorithm for R-tree based on Objects’ Locations Distribution

Al-Nsour

Sleit

Alshraideh

2018

Journal of Information Science

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Spatial data indexing methods are of extreme importance as they massively build up as a result of the explosive growth in capturing data with spatial features. No matter how much the data size is, eventually it will reside on disk pages. Disk pages have to be properly indexed to preserve spatial properties of objects, optimise disk space usage and improve objects’ retrieval performance. One of the most popular spatial data indexes is the R-tree which is a height balanced tree data structure, where leaf nodes resemble disk pages and contain pointers to objects’ locations. A single tree node can host up to a maximum number of objects, where any more insertion makes it an overflown node and it has to be split. Better splits lead to better index performance and more utilisation of disk space. In this work, we introduce a new way of finding the most proper split for an overflown node in the R-tree index. The proposed work scans – in a linear cost – the overflown node’s objects once to identify the distribution of objects’ locations (minimum bounding rectangles (MBRs)) in relative to its node’s bounding rectangle (node’s MBR). It uses objects’ locations to calculate – for each main axis – the split quality factors: expected overlap between resulting nodes, objects distribution evenness among resulting nodes and the perimeter of resulting nodes. The axis with better combined quality factors values is selected as the split axis. The Splitting based on Objects’ Locations Distribution (SOLD) algorithm was implemented and tested against two other splitting algorithms, experiments using synthetic and real data files showed good results and it outperformed both algorithms in index creation tests and data retrieval tests.

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Section: Related Workmentioning

confidence: 99%

SOLD: A node-Splitting algorithm for R-tree based on Objects’ Locations Distribution

Al-Nsour

Sleit

Alshraideh

2018

Journal of Information Science

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“…Node splitting leads to minimizing the sum of the areas of the two resulting nodes. Several node splitting algorithms exist in the literature [24–26].…”

Section: Related Workmentioning

confidence: 99%

Multi Small Index (MSI): A spatial indexing structure

Al-Badarneh

Al-Alaj

Mahafzah

2013

Journal of Information Science

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Most of the existing spatial indices are constructed using a single hierarchal index structure; hence a large number of index pages (nodes) are most likely to be inspected during spatial query execution. Since spatial queries usually fetch spatial objects based on their spatial position in the space, it is significant that spatial objects are clustered in such a way that pertinent objects to a query are fetched quickly. This paper presents a method for partitioning the whole space into set of small subspaces. Then, an index structure for each subspace (called the Multi Small Index) is built. This makes it is easy to quickly retrieve spatial objects that are relevant to the query in question using their corresponding small spatial index structures and ignoring other irrelevant indices. To evaluate our new approach, we conducted a set of experimental studies using a collection of real-life spatial datasets (TIGER data files) with diverse sizes and different object sizes, densities and distributions, as well as various query sizes. The results show that (using small query sizes) our proposed structure (Multi Small Index) outperforms the original R-tree (Single Big Index) structure, achieving nearly 50% saving in disk access.

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