Localized co-occurrence model for fast approximate search in 3D structure databases

Huang, Zi, Shen, Heng Tao and Zhou, Xiaofang (2008) Localized co-occurrence model for fast approximate search in 3D structure databases. IEEE Transactions on Knowledge and Data Engineering, 20 4: 519-531. doi:10.1109/TKDE.2007.190729

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Author Huang, Zi
Shen, Heng Tao
Zhou, Xiaofang
Title Localized co-occurrence model for fast approximate search in 3D structure databases
Journal name IEEE Transactions on Knowledge and Data Engineering   Check publisher's open access policy
ISSN 1041-4347
Publication date 2008-04-01
Year available 2008
Sub-type Article (original research)
DOI 10.1109/TKDE.2007.190729
Open Access Status
Volume 20
Issue 4
Start page 519
End page 531
Total pages 13
Editor F. B. Bastani
S. McConnell
Place of publication Piscataway NJ, USA
Publisher IEEE Computer Society
Language eng
Subject C1
0806 Information Systems
890205 Information Processing Services (incl. Data Entry and Capture)
Abstract Similarity search for 3D structure data sets is fundamental to many database applications such as molecular biology, image registration, and computer-aided design. Identifying the common 3D subtructures between two objects is an important research problem. However, it is well known that computing structural similarity is very expensive due to the high exponential time complexity of structure similarity measures. As the structure databases keep growing rapidly, real-time search from large-structure databases becomes problematic. In this paper, we present a novel statistical model, that is, the multiresolution Localized Co-Occurrence Model (LCM), to approximately measure the similarity between the two point-based 3D structures in linear time complexity for fast retrieval. LCM could capture both distribution characteristics and spatial structure of 3D data by localizing the point co-occurrence relationship within a predefined neighborhood system. As a step further, a novel structure query processing method called the incremental and Bounded search (iBound) is also proposed to speed up the search process. iBound avoids a large amount of expensive computation at higher resolution LCMs. By superposing two LCMs, their largest common substructure can also be found quickly. Finally, our experiment results prove the effectiveness and efficiency of our methods.
Keyword distributed databases
query processing
statistical analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Fri, 17 Apr 2009, 22:42:39 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering