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Indexing Network Voronoi Diagrams

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Database Systems for Advanced Applications (DASFAA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7238))

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Abstract

The Network Voronoi diagram and its variants have been extensively used in the context of numerous applications in road networks, particularly to efficiently evaluate various spatial proximity queries such as k nearest neighbor (kNN), reverse kNN, and closest pair. Although the existing approaches successfully utilize the network Voronoi diagram as a way to partition the space for their specific problems, there is little emphasis on how to efficiently find and access the network Voronoi cell containing a particular point or edge of the network. In this paper, we study the index structures on network Voronoi diagrams that enable exact and fast response to contain query in road networks. We show that existing index structures, treating a network Voronoi cell as a simple polygon, may yield inaccurate results due to the network topology, and fail to scale to large networks with numerous Voronoi generators. With our method, termed Voronoi-Quad-tree (or VQ-tree for short), we use Quad-tree to index network Voronoi diagrams to address both of these shortcomings. We demonstrate the efficiency of VQ-tree via experimental evaluations with real-world datasets consisting of a variety of large road networks with numerous data objects.

This research has been funded in part by NSF grants IIS-0238560 (PECASE), IIS-0534761,IIS-0742811 and CNS-0831505 (CyberTrust), and in part from the METRANS Transportation Center, under grants from USDOT and Caltrans.Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We thank Prof. Ulrich Neumann for his insightful discussions and comments.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Southern California, Los Angeles, CA, 90089-0781, USA

    Ugur Demiryurek & Cyrus Shahabi

Authors
  1. Ugur Demiryurek
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  2. Cyrus Shahabi
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, 151747, Seoul, South Korea

    Sang-goo Lee

  2. Computer School, Wuhan University, Luo-jia-shan, Wuchang, 430081, Wuhan, Hubei Province, China

    Zhiyong Peng

  3. School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  4. Department of Computer Science, Kangwon National University, 192-1, Hyoja2-Dong, Chuncheon, 200701, Kangwon, South Korea

    Yang-Sae Moon

  5. Institute for Computer Science and Business Information, University of Duisburg-Essen, Schützenbahn 70, 45117, Essen, Germany

    Rainer Unland

  6. School of Information and Communication Engineering, Chungbuk National University, 52 Naesudong-ro, Heungdeok-gu, Cheongju, 4072, Chungbuk, South Korea

    Jaesoo Yoo

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Demiryurek, U., Shahabi, C. (2012). Indexing Network Voronoi Diagrams. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_38

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  • DOI: https://doi.org/10.1007/978-3-642-29038-1_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29037-4

  • Online ISBN: 978-3-642-29038-1

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