Browsing by Author "Bin CUI"

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Supporting Frequent Updates in R-Trees: A Bottom-Up Approach
    (2004-04-01T00:00:00Z) Mong Li LEE; Wynne HSU; Christian S. JENSEN; Bin CUI; Keng Lik TEO
    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.