New Query Processing Algorithms for Range and k-NN Search in Spatial Network Databases
Identifieur interne : 005C19 ( Istex/Corpus ); précédent : 005C18; suivant : 005C20New Query Processing Algorithms for Range and k-NN Search in Spatial Network Databases
Auteurs : Jae-Woo Chang ; Yong-Ki Kim ; Sang-Mi Kim ; Young-Chang KimSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: In this paper, we design the architecture of disk-based data structures for spatial network databases (SNDB). Based on this architecture, we propose new query processing algorithms for range search and k nearest neighbors (k-NN) search, depending on the density of point of interests (POIs) in the spatial network. For this, we effectively combine Euclidean restriction and the network expansion techniques according to the density of POIs. In addition, our two query processing algorithms can reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using maintaining the shortest network distances of all the nodes in the spatial network. It is shown that our range query processing algorithm achieves about up to one order of magnitude better performance than the existing range query processing algorithm, such as RER and RNE [1]. In addition, our k-NN query processing algorithm achieves about up to 170~400% performance improvements over the existing network expansion k-NN algorithm, called INE, while it shows about up to one order of magnitude better performance than the existing Euclidean restriction k-NN algorithm, called IER [1].
Url:
DOI: 10.1007/11908883_16
Links to Exploration step
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<front><div type="abstract" xml:lang="en">Abstract: In this paper, we design the architecture of disk-based data structures for spatial network databases (SNDB). Based on this architecture, we propose new query processing algorithms for range search and k nearest neighbors (k-NN) search, depending on the density of point of interests (POIs) in the spatial network. For this, we effectively combine Euclidean restriction and the network expansion techniques according to the density of POIs. In addition, our two query processing algorithms can reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using maintaining the shortest network distances of all the nodes in the spatial network. It is shown that our range query processing algorithm achieves about up to one order of magnitude better performance than the existing range query processing algorithm, such as RER and RNE [1]. In addition, our k-NN query processing algorithm achieves about up to 170~400% performance improvements over the existing network expansion k-NN algorithm, called INE, while it shows about up to one order of magnitude better performance than the existing Euclidean restriction k-NN algorithm, called IER [1].</div>
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<abstract>Abstract: In this paper, we design the architecture of disk-based data structures for spatial network databases (SNDB). Based on this architecture, we propose new query processing algorithms for range search and k nearest neighbors (k-NN) search, depending on the density of point of interests (POIs) in the spatial network. For this, we effectively combine Euclidean restriction and the network expansion techniques according to the density of POIs. In addition, our two query processing algorithms can reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using maintaining the shortest network distances of all the nodes in the spatial network. It is shown that our range query processing algorithm achieves about up to one order of magnitude better performance than the existing range query processing algorithm, such as RER and RNE [1]. In addition, our k-NN query processing algorithm achieves about up to 170~400% performance improvements over the existing network expansion k-NN algorithm, called INE, while it shows about up to one order of magnitude better performance than the existing Euclidean restriction k-NN algorithm, called IER [1].</abstract>
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<editor><persName><forename type="first">Hyoil</forename>
<surname>Han</surname>
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<email>hhan@ischool.drexel.edu</email>
<affiliation>Drexel University, USA</affiliation>
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<editor><persName><forename type="first">Martin</forename>
<surname>Hepp</surname>
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<email>mhepp@computer.org</email>
<affiliation>E-Business and Web Science Research Group, Bundeswehr University Munich, Germany</affiliation>
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<editor><persName><forename type="first">Miltiadis</forename>
<forename type="first">D.</forename>
<surname>Lytras</surname>
</persName>
<email>miltiadis.lytras@gmail.com</email>
<affiliation>University of Patras, Argolidos 40-42, 153-44, Gerakas Attikis, Greece</affiliation>
</editor>
<editor><persName><forename type="first">Vojislav</forename>
<forename type="first">B.</forename>
<surname>Mišić</surname>
</persName>
<email>vmisic@cs.umanitoba.ca</email>
<affiliation>University of Manitoba, Canada</affiliation>
</editor>
<editor><persName><forename type="first">Geert</forename>
<surname>Poels</surname>
</persName>
<email>geert.poels@ugent.be</email>
<affiliation>Ghent University, Belgium</affiliation>
</editor>
<editor><persName><forename type="first">Il-Yeol</forename>
<surname>Song</surname>
</persName>
<email>song@drexel.edu</email>
<affiliation>College of Information Science and Technology, Drexel University, USA</affiliation>
</editor>
<editor><persName><forename type="first">Juan</forename>
<surname>Trujillo</surname>
</persName>
<email>jtrujillo@dlsi.ua.es</email>
<affiliation>Department of Software and Computing Systems, University of Alicante, Spain</affiliation>
</editor>
<editor><persName><forename type="first">Christelle</forename>
<surname>Vangenot</surname>
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<email>christelle.vangenot@epfl.ch</email>
<affiliation>EPFL, Switzerland</affiliation>
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<editor><persName><forename type="first">David</forename>
<surname>Hutchison</surname>
</persName>
<affiliation>Lancaster University, UK</affiliation>
</editor>
<editor><persName><forename type="first">Takeo</forename>
<surname>Kanade</surname>
</persName>
<affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation>
</editor>
<editor><persName><forename type="first">Josef</forename>
<surname>Kittler</surname>
</persName>
<affiliation>University of Surrey, Guildford, UK</affiliation>
</editor>
<editor><persName><forename type="first">Jon</forename>
<forename type="first">M.</forename>
<surname>Kleinberg</surname>
</persName>
<affiliation>Cornell University, Ithaca, NY, USA</affiliation>
</editor>
<editor><persName><forename type="first">Friedemann</forename>
<surname>Mattern</surname>
</persName>
<affiliation>ETH Zurich, Switzerland</affiliation>
</editor>
<editor><persName><forename type="first">John</forename>
<forename type="first">C.</forename>
<surname>Mitchell</surname>
</persName>
<affiliation>Stanford University, CA, USA</affiliation>
</editor>
<editor><persName><forename type="first">Moni</forename>
<surname>Naor</surname>
</persName>
<affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation>
</editor>
<editor><persName><forename type="first">Oscar</forename>
<surname>Nierstrasz</surname>
</persName>
<affiliation>University of Bern, Switzerland</affiliation>
</editor>
<editor><persName><forename type="first">C.</forename>
<surname>Pandu Rangan</surname>
</persName>
<affiliation>Indian Institute of Technology, Madras, India</affiliation>
</editor>
<editor><persName><forename type="first">Bernhard</forename>
<surname>Steffen</surname>
</persName>
<affiliation>University of Dortmund, Germany</affiliation>
</editor>
<editor><persName><forename type="first">Madhu</forename>
<surname>Sudan</surname>
</persName>
<affiliation>Massachusetts Institute of Technology, MA, USA</affiliation>
</editor>
<editor><persName><forename type="first">Demetri</forename>
<surname>Terzopoulos</surname>
</persName>
<affiliation>University of California, Los Angeles, CA, USA</affiliation>
</editor>
<editor><persName><forename type="first">Dough</forename>
<surname>Tygar</surname>
</persName>
<affiliation>University of California, Berkeley, CA, USA</affiliation>
</editor>
<editor><persName><forename type="first">Moshe</forename>
<forename type="first">Y.</forename>
<surname>Vardi</surname>
</persName>
<affiliation>Rice University, Houston, TX, USA</affiliation>
</editor>
<editor><persName><forename type="first">Gerhard</forename>
<surname>Weikum</surname>
</persName>
<affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation>
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<biblScope><date>2006</date>
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<idno type="pISSN">0302-9743</idno>
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<abstract xml:lang="en"><p>Abstract: In this paper, we design the architecture of disk-based data structures for spatial network databases (SNDB). Based on this architecture, we propose new query processing algorithms for range search and k nearest neighbors (k-NN) search, depending on the density of point of interests (POIs) in the spatial network. For this, we effectively combine Euclidean restriction and the network expansion techniques according to the density of POIs. In addition, our two query processing algorithms can reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using maintaining the shortest network distances of all the nodes in the spatial network. It is shown that our range query processing algorithm achieves about up to one order of magnitude better performance than the existing range query processing algorithm, such as RER and RNE [1]. In addition, our k-NN query processing algorithm achieves about up to 170~400% performance improvements over the existing network expansion k-NN algorithm, called INE, while it shows about up to one order of magnitude better performance than the existing Euclidean restriction k-NN algorithm, called IER [1].</p>
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<Affiliation ID="Aff31"><OrgDivision>Dept. of Computer Engineering</OrgDivision>
<OrgName>Chonbuk National Univ.</OrgName>
<OrgAddress><City>Chonju, Chonbuk</City>
<Postcode>561-756</Postcode>
<Country>South Korea</Country>
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<Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading>
<Para>In this paper, we design the architecture of disk-based data structures for spatial network databases (SNDB). Based on this architecture, we propose new query processing algorithms for range search and k nearest neighbors (k-NN) search, depending on the density of point of interests (POIs) in the spatial network. For this, we effectively combine Euclidean restriction and the network expansion techniques according to the density of POIs. In addition, our two query processing algorithms can reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using maintaining the shortest network distances of all the nodes in the spatial network. It is shown that our range query processing algorithm achieves about up to one order of magnitude better performance than the existing range query processing algorithm, such as RER and RNE [1]. In addition, our k-NN query processing algorithm achieves about up to 170~400% performance improvements over the existing network expansion k-NN algorithm, called INE, while it shows about up to one order of magnitude better performance than the existing Euclidean restriction k-NN algorithm, called IER [1].</Para>
</Abstract>
<ArticleNote Type="Misc"><SimplePara>This work is financially supported by the Ministry of Education and Human Resources Development(MOE), the Ministry of Commerce, Industry and Energy(MOCIE) and the Ministry of Labor(MOLAB) though the fostering project of the Lab of Excellency.</SimplePara>
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<name type="personal"><namePart type="given">Jae-Woo</namePart>
<namePart type="family">Chang</namePart>
<affiliation>Dept. of Computer Engineering, Chonbuk National Univ., 561-756, Chonju, Chonbuk, South Korea</affiliation>
<affiliation>E-mail: jwchang@chonbuk.ac.kr</affiliation>
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<name type="personal"><namePart type="given">Yong-Ki</namePart>
<namePart type="family">Kim</namePart>
<affiliation>Dept. of Computer Engineering, Chonbuk National Univ., 561-756, Chonju, Chonbuk, South Korea</affiliation>
<affiliation>E-mail: ykkim@dblab.chonbuk.ac.kr</affiliation>
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<name type="personal"><namePart type="given">Sang-Mi</namePart>
<namePart type="family">Kim</namePart>
<affiliation>Dept. of Computer Engineering, Chonbuk National Univ., 561-756, Chonju, Chonbuk, South Korea</affiliation>
<affiliation>E-mail: smkim@dblab.chonbuk.ac.kr</affiliation>
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<name type="personal"><namePart type="given">Young-Chang</namePart>
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<abstract lang="en">Abstract: In this paper, we design the architecture of disk-based data structures for spatial network databases (SNDB). Based on this architecture, we propose new query processing algorithms for range search and k nearest neighbors (k-NN) search, depending on the density of point of interests (POIs) in the spatial network. For this, we effectively combine Euclidean restriction and the network expansion techniques according to the density of POIs. In addition, our two query processing algorithms can reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using maintaining the shortest network distances of all the nodes in the spatial network. It is shown that our range query processing algorithm achieves about up to one order of magnitude better performance than the existing range query processing algorithm, such as RER and RNE [1]. In addition, our k-NN query processing algorithm achieves about up to 170~400% performance improvements over the existing network expansion k-NN algorithm, called INE, while it shows about up to one order of magnitude better performance than the existing Euclidean restriction k-NN algorithm, called IER [1].</abstract>
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