The RS-tree: An efficient data structure for distance browsing queries

Park, Dongjoo; Heu, Shin; Kim, Hyoung-Joo

doi:10.1016/s0020-0190(01)00163-6

Cited by 3 publications

(3 citation statements)

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“…The most common way of indexing spatio-temporal data is based on tree indexing. The RS-tree [28], X-tree [5], weRtree [6], NV-tree [23], SMILe-tree [22], Bx-tree [16], ST 2 B -tree [8], TPR-tree [32], TPR*-tree [35], and 2 n index tree [39] are tree-based indexing methodologies.…”

Section: Tree-based Indexingmentioning

confidence: 99%

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Spatio-temporal querying recurrent multimedia databases using a semantic sequence state graph

2011

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We present an indexing method for spatiotemporal data: semantic sequence state graph (S 3 G). S 3 G maintains objects with their locations as states and events as transitions. The spatial information is maintained in states while the semantic events result in temporal ordering between the states. If the objects visit the same locations repeatedly, we call such databases as recurrent databases. Our querying interface supports queries based on spatiotemporal logic that includes operators such as 'next' and 'eventually'. The interactive querying interface enables the user to build the query interactively and see the intermediate results of the query.

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Section: Tree-based Indexingmentioning

confidence: 99%

“…2.1.1.1 R-tree-based indexing RS-tree [28] uses R-tree for spatial properties. The non-spatial properties are indexed with respect to R-tree using an S-tree which has the same structure to prune the results of querying the R-tree given non-spatial properties.…”

Section: Tree-based Indexingmentioning

confidence: 99%

Spatio-temporal querying recurrent multimedia databases using a semantic sequence state graph

2011

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“…Special attention should be devoted to the use of signatures in connection with R-trees. The approach [9] offers an RS-tree that consists of an R-tree and an S-tree [10], i.e. a well-know hierarchical signature file.…”

Section: R-tree and Its Variantsmentioning

confidence: 99%

Efficient Processing of Narrow Range Queries in Multi-dimensional Data Structures

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et al. 2006

2006 10th International Database Engineering and Applications Symposium (IDEAS'06)

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Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing of text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing: data structures to indexing metric and vector spaces. R-trees, R*-trees and (B)UB-trees are representatives of the vector data structures. These data structures provide efficient processing of many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned, the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is an important type of the range query. Its processing is inefficient in vector data structures. Moreover, the efficiency decreases as the dimension of the indexed space increases. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the multi-dimensional data structures like R-tree or UB-tree but original functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the signature data structure, e.g., Signature R-tree or Signature UB-tree.

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