Distributed and Parallel Databases

, Volume 34, Issue 2, pp 259–287 | Cite as

Probabilistic nearest neighbor query processing on distributed uncertain data

  • Daichi Amagata
  • Yuya Sasaki
  • Takahiro Hara
  • Shojiro Nishio
Article

Abstract

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.

Keywords

Probabilistic nearest neighbor query Uncertain databases Distributed query processing 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Daichi Amagata
    • 1
  • Yuya Sasaki
    • 2
  • Takahiro Hara
    • 1
  • Shojiro Nishio
    • 1
  1. 1.Department of Multimedia Engineering Graduate School of Information Science and Technology Osaka UniversitySuitaJapan
  2. 2.Department of Systems and Social InformaticsGraduate School of Information Science Nagoya UniversityNagoyaJapan

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