An Efficient In-Memory R-Tree Construction Scheme for Spatio-Temporal Data Stream

  • Ting Zhang
  • Lianghuai YangEmail author
  • Donghai Shen
  • Yulei Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)


In this paper, we proposed an efficient R-tree construction method by bulk loading over spatial-temporal data stream. The core idea is to partition spatial-temporal data stream into time windows and construct an R-tree for each time window. In each time window, we parallelized space partitioning and data stream reception during R-tree construction; and then we adopted sorting-based bulk loading scheme to optimize R-tree construction, which avoided unnecessary synchronization overhead and accelerated the R-tree construction. In addition, to reduce the sorting cost of R-tree bulk loading, sampling-based space partitioning scheme was introduced. Theoretical analysis and experiments demonstrated the effectiveness of our proposed method.


R-tree Data stream In-memory index Big data 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ting Zhang
    • 1
  • Lianghuai Yang
    • 1
    Email author
  • Donghai Shen
    • 1
  • Yulei Fan
    • 1
  1. 1.School of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina

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