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Supporting K nearest neighbors query on high-dimensional data in P2P systems

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Abstract

Peer-to-peer systems have been widely used for sharing and exchanging data and resources among numerous computer nodes. Various data objects identifiable with high dimensional feature vectors, such as text, images, genome sequences, are starting to leverage P2P technology. Most of the existing works have been focusing on queries on data objects with one or few attributes and thus are not applicable on high dimensional data objects. In this study, we investigate K nearest neighbors query (KNN) on high dimensional data objects in P2P systems. Efficient query algorithm and solutions that address various technical challenges raised by high dimensionality, such as search space resolution and incremental search space refinement, are proposed. An extensive simulation using both synthetic and real data sets demonstrates that our proposal efficiently supports KNN query on high dimensional data in P2P systems.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Pennsylvania State University, Philadelphia, PA, 16802, USA

    Mei Li, Wang-Chien Lee & Anand Sivasubramaniam

  2. Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

    Jizhong Zhao

Authors
  1. Mei Li
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  2. Wang-Chien Lee
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  3. Anand Sivasubramaniam
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  4. Jizhong Zhao
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Correspondence to Wang-Chien Lee.

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Li, M., Lee, WC., Sivasubramaniam, A. et al. Supporting K nearest neighbors query on high-dimensional data in P2P systems. Front. Comput. Sci. China 2, 234–247 (2008). https://doi.org/10.1007/s11704-008-0026-7

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  • DOI: https://doi.org/10.1007/s11704-008-0026-7

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