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Distributed K-Distance Indexing Approach for Efficient Shortest Path Discovery on Large Graphs

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Database Systems for Advanced Applications (DASFAA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8505))

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Abstract

The emergence of large real life networks such as social networks, web page links, and traffic networks exhibits complex graph structures with millions of vertices and edges. Among many operations for exploiting these graphs, the shortest path discovery is a major and expensive one. Besides the in-memory approaches, many efficient shortest path computation methods have been developed on top of distributed and parallel platforms. Pregel, a bulk synchronous parallel framework, is one of them for processing large graphs. The known shortest path computation approach with Pregel is computation intensive and unable to target real-time services. In this paper, we propose a Pregel based efficient k-distance index technique that allows efficient single pair shortest path discovery. We reduce the network cost and unnecessary operations by transmitting more information in a single superstep. The extensive experiments on both real and synthetic datasets reveal the superiority of the proposed approach.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2013R1A2A1A05056375).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, Seocheon-Dong, Giheung-Gu, Yongin-Si, 449-701, Korea

    Jihye Hong, Hyunwook Kim, Waqas Nawaz, Kisung Park, Byeong-Soo Jeong & Young-Koo Lee

Authors
  1. Jihye Hong
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  2. Hyunwook Kim
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  3. Waqas Nawaz
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  4. Kisung Park
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  5. Byeong-Soo Jeong
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  6. Young-Koo Lee
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Corresponding author

Correspondence to Young-Koo Lee .

Editor information

Editors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea, Republic of (South Korea)

    Wook-Shin Han

  2. National University of Singapore, Singapore, Singapore

    Mong Li Lee

  3. Udayana University, Badung, Indonesia

    Agus Muliantara

  4. Udayana University, Badung, Indonesia

    Ngurah Agus Sanjaya

  5. Christian-Albrechts-Universität zu Kiel Institut für Informatik, Kiel, Germany

    Bernhard Thalheim

  6. Fudan University, Shanghai, China

    Shuigeng Zhou

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Hong, J., Kim, H., Nawaz, W., Park, K., Jeong, BS., Lee, YK. (2014). Distributed K-Distance Indexing Approach for Efficient Shortest Path Discovery on Large Graphs. In: Han, WS., Lee, M., Muliantara, A., Sanjaya, N., Thalheim, B., Zhou, S. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science(), vol 8505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43984-5_6

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  • DOI: https://doi.org/10.1007/978-3-662-43984-5_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43983-8

  • Online ISBN: 978-3-662-43984-5

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