The SH-tree: A Super Hybrid Index Structure for Multidimensional Data

Khanh Tran Dang; Josef Küng; Roland Wagner

doi:10.1007/3-540-44759-8_34

The SH-tree: A Super Hybrid Index Structure for Multidimensional Data

Khanh Tran Dang
, Josef Küng
, Roland Wagner

Institute for Application-oriented Knowledge Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Nowadays feature vector based similarity search is increasingly emerging in database systems. Consequently, many multidimensional data index techniques have been widely introduced to database researcher community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Recently, a hybrid index structure has been proposed. It combines both SP/KD-tree-based and DP/R-tree-based techniques to form a new, more efficient index structure. However, weaknesses are still existing in techniques above. In this paper, we introduce a novel and flexible index structure for multidimensional data, the SH-tree (Super Hybrid tree). Theoretical analyses show that the SH-tree is a good combination of both techniques with respect to both presentation and search algorithms. It overcomes the shortcomings and makes use of their positive aspects to facilitate efficient similarity searches.

Original language	English
Title of host publication	Database and Expert Systems Applications: 12th International Conference, DEXA 2001 Munich, Germany, September 3-5, 2001 Proceedings
Editors	Heinrich C. Mayr, Jiri Lazansky, Gerald Quirchmayr, Pavel Vogel
Publisher	Springer Verlag
Pages	340-349
Number of pages	10
ISBN (Print)	3540425276, 9783540425274
DOIs	https://doi.org/10.1007/3-540-44759-8_34
Publication status	Published - Sept 2001

Publication series

Name	Lecture Notes in Computer Science
Volume	2113
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Fields of science

102001 Artificial intelligence
102006 Computer supported cooperative work (CSCW)
102010 Database systems
102014 Information design
102015 Information systems
102016 IT security
102028 Knowledge engineering
102019 Machine learning
102022 Software development
102025 Distributed systems
502007 E-commerce
505002 Data protection
506002 E-government
509018 Knowledge management
202007 Computer integrated manufacturing (CIM)
102033 Data mining
102035 Data science

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10.1007/3-540-44759-8_34

Cite this

Dang, K. T., Küng, J., & Wagner, R. (2001). The SH-tree: A Super Hybrid Index Structure for Multidimensional Data. In H. C. Mayr, J. Lazansky, G. Quirchmayr, & P. Vogel (Eds.), Database and Expert Systems Applications: 12th International Conference, DEXA 2001 Munich, Germany, September 3-5, 2001 Proceedings (pp. 340-349). (Lecture Notes in Computer Science; Vol. 2113). Springer Verlag. https://doi.org/10.1007/3-540-44759-8_34

Dang, Khanh Tran ; Küng, Josef ; Wagner, Roland. / The SH-tree: A Super Hybrid Index Structure for Multidimensional Data. Database and Expert Systems Applications: 12th International Conference, DEXA 2001 Munich, Germany, September 3-5, 2001 Proceedings. editor / Heinrich C. Mayr ; Jiri Lazansky ; Gerald Quirchmayr ; Pavel Vogel. Springer Verlag, 2001. pp. 340-349 (Lecture Notes in Computer Science).

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abstract = "Nowadays feature vector based similarity search is increasingly emerging in database systems. Consequently, many multidimensional data index techniques have been widely introduced to database researcher community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Recently, a hybrid index structure has been proposed. It combines both SP/KD-tree-based and DP/R-tree-based techniques to form a new, more efficient index structure. However, weaknesses are still existing in techniques above. In this paper, we introduce a novel and flexible index structure for multidimensional data, the SH-tree (Super Hybrid tree). Theoretical analyses show that the SH-tree is a good combination of both techniques with respect to both presentation and search algorithms. It overcomes the shortcomings and makes use of their positive aspects to facilitate efficient similarity searches.",

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Dang, KT, Küng, J & Wagner, R 2001, The SH-tree: A Super Hybrid Index Structure for Multidimensional Data. in HC Mayr, J Lazansky, G Quirchmayr & P Vogel (eds), Database and Expert Systems Applications: 12th International Conference, DEXA 2001 Munich, Germany, September 3-5, 2001 Proceedings. Lecture Notes in Computer Science, vol. 2113, Springer Verlag, pp. 340-349. https://doi.org/10.1007/3-540-44759-8_34

The SH-tree: A Super Hybrid Index Structure for Multidimensional Data. / Dang, Khanh Tran; Küng, Josef ; Wagner, Roland.
Database and Expert Systems Applications: 12th International Conference, DEXA 2001 Munich, Germany, September 3-5, 2001 Proceedings. ed. / Heinrich C. Mayr; Jiri Lazansky; Gerald Quirchmayr; Pavel Vogel. Springer Verlag, 2001. p. 340-349 (Lecture Notes in Computer Science; Vol. 2113).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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AB - Nowadays feature vector based similarity search is increasingly emerging in database systems. Consequently, many multidimensional data index techniques have been widely introduced to database researcher community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Recently, a hybrid index structure has been proposed. It combines both SP/KD-tree-based and DP/R-tree-based techniques to form a new, more efficient index structure. However, weaknesses are still existing in techniques above. In this paper, we introduce a novel and flexible index structure for multidimensional data, the SH-tree (Super Hybrid tree). Theoretical analyses show that the SH-tree is a good combination of both techniques with respect to both presentation and search algorithms. It overcomes the shortcomings and makes use of their positive aspects to facilitate efficient similarity searches.

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Dang KT, Küng J , Wagner R. The SH-tree: A Super Hybrid Index Structure for Multidimensional Data. In Mayr HC, Lazansky J, Quirchmayr G, Vogel P, editors, Database and Expert Systems Applications: 12th International Conference, DEXA 2001 Munich, Germany, September 3-5, 2001 Proceedings. Springer Verlag. 2001. p. 340-349. (Lecture Notes in Computer Science). doi: 10.1007/3-540-44759-8_34

The SH-tree: A Super Hybrid Index Structure for Multidimensional Data

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