Supporting frequent updates in R-trees: a bottom-up approach

  • Authors:

  • Mong Li Lee;Wynne Hsu;Christian S. Jensen;Bin Cui;Keng Lik Teo

  • Affiliations:

  • School of Computing, National University of Singapore, Singapore;School of Computing, National University of Singapore, Singapore;Department of Computer Science, Aalborg University, Denmark;School of Computing, National University of Singapore, Singapore;School of Computing, National University of Singapore, Singapore

  • Venue:
  • VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
  • Year:
  • 2003

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Abstract

Advances in hardware-related technologies promise to enable new data management applications that monitor continuous processes. In these applications, enormous amounts of state samples are obtained via sensors and are streamed to a database. Further, updates are very frequent and may exhibit locality. While the R-tree is the index of choice for multi-dimensional data with low dimensionality, and is thus relevant to these applications, R-tree updates are also relatively inefficient. We present a bottom-up update strategy for R-trees that generalizes existing update techniques and aims to improve update performance. It has different levels of reorganization--ranging from global to local--during updates, avoiding expensive top-down updates. A compact main-memory summary structure that allows direct access to the R-tree index nodes is used together with efficient bottom-up algorithms. Empirical studies indicate that the bottom-up strategy outperforms the traditional top-down technique, leads to indices with better query performance, achieves higher throughput, and is scalable.