A revised r*-tree in comparison with related index structures

Beckmann, Norbert; Seeger, Bernhard

doi:10.1145/1559845.1559929

Cited by 71 publications

(42 citation statements)

References 15 publications

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“…This study prompted a wave of research papers and one can say that it gave birth to the new area of research. This research related to development of the new R-Tree variants [1,8,9], niche approaches [6,9], split techniques [10][11][12], concurrency techniques [13,14] etc. The study [6] states that there are several dozens of R-Tree variants.…”

Section: Fig 1: R-tree Examplementioning

confidence: 99%

See 1 more Smart Citation

To sort or not to sort: the evaluation of R-Tree and B+-Tree in transactional environment with ordered result requirement

Chernishev

Smirnov

Fedotovsky³

et al. 2014

Proceedings of ISP RAS

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Section: Fig 1: R-tree Examplementioning

confidence: 99%

“…We are mainly interested in R-Tree because of its popularity in commercial DBMS systems [1]: PostgreSQL, Oracle, Informix, SQLite and MySQL use this approach. This interest proves, that despite being rather old (more than 25 years), R-Tree still may be called industrial-strength technology.…”

Section: Introductionmentioning

confidence: 99%

To sort or not to sort: the evaluation of R-Tree and B+-Tree in transactional environment with ordered result requirement

Chernishev

Smirnov

Fedotovsky³

et al. 2014

Proceedings of ISP RAS

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“…Among these research efforts, R-tree [13] and its variants [4,5] have been successfully adopted in many systems. Their partitioning and traversal logic have also inspired distributed indexing schemes [11,25].…”

Section: Related Workmentioning

confidence: 99%

Robust distributed indexing for locality-skewed workloads

Lee

Hwang

2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Multidimensional indexing is crucial for enabling a fast search over large-scale data. Owing to the unprecedented scale of data, extending such indexing technology has recently gained attention in distributed environments. The goal of existing efforts in distributed indexing has been the localization of queries to data residing at a small number of nodes (i.e., locality-preserving indexing) to minimize communication cost. However, considering that workloads often correlate with data locality, such indexing often generates hotspots. Location-based queries are typically skewed to disaster areas during certain periods of time, e.g., during Hurricane Irene, search traffic increased by more than 2000%. To alleviate such hotspots, we propose workload-balancing as an optimization goal. A cost model analytically supporting the need for load balancing is first developed, then a distributed index that evenly distributes the workload is presented. Our empirical study suggests that hotspots degrading search performance can be effectively alleviated. Specifically, when deployed to Amazon EC2, our proposed scheme showed maximum speed-up of 127.7%. Even in hostile settings where workload is not at all correlated with the search criteria, the proposed scheme's performance is comparable to existing approaches optimized for such settings.

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“…Previous work [4][5][6][7] showed that although there are a few pruning metrics [10,11] for kNN search task, branch-andbound approach have been widely used in MBR based similarity search. However, when the dimensionality is high, the computation cost of MINDIST and MINMAXDIST become costly, thus many approaches proposed to speed-up similarity queries.…”

Section: Related Workmentioning

confidence: 99%

“…Multidimensional feature vectors of multimedia objects often represented by MBRs (Minimum Bounding Rectangle), they are typically organized by certain indexing techniques, and often employ pruning techniques to support efficient multidimensional similarity query. R*-Tree [3] and its variations [4][5][6]19], may be the most popular MBR based multidimensional index structures that support efficient similarity search.…”

Section: Introductionmentioning

confidence: 99%

Reduce Pruning Cost to Accelerate Multimedia kNN Search over MBRs Based Index Structures

Zhang

Wang

2011

2011 Third International Conference on Multimedia Information Networking and Security

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MBR (Minimum Bounding Rectangle) has been widely used to represent multimedia data objects for multimedia indexing techniques. In kNN search, MINDIST and MINMAXDIST was the most popular pruning metrics employed by MBR based index structures. However, with the increase of dimensionality, the computation costs of above two metrics are expensive, and the involved distance computations may become the considerable component. In this paper, to improve the performance of kNN search over multimedia data, we proposed an approach to reduce the computation cost of pruning heuristics by use the cheap upper bound of MINMAXDIST instead of its precise value, and then we derive enhanced cheap heuristics based on this upper bound for kNN pruning, which are helpful to avoid visiting unnecessary data objects and MBRs. We conducted an extensive experimental study on a large real-life multimedia dataset called MSRA-MM 2.0, obtained from the commercial Bing Image search engine. The results show that the proposed approach significantly reduced the computation cost and boosted the overall performance of MBR based multimedia kNN search.

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A revised r*-tree in comparison with related index structures

Cited by 71 publications

References 15 publications

To sort or not to sort: the evaluation of R-Tree and B+-Tree in transactional environment with ordered result requirement

To sort or not to sort: the evaluation of R-Tree and B+-Tree in transactional environment with ordered result requirement

Robust distributed indexing for locality-skewed workloads

Reduce Pruning Cost to Accelerate Multimedia kNN Search over MBRs Based Index Structures

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