Managing Frequent Updates in R-Trees for Update-Intensive Applications

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Abstract

Managing frequent updates is greatly important in many update-intensive applications, such as location-aware services,sensor networks, and stream databases. In this paper, we present an R-tree-based index structure (called Rsb-tree, R-tree withsemibulk loading) for efficiently managing frequent updates from massive moving objects. The concept of semibulk loading isexploiting a small in-memory buffer to defer, buffer, and group the incoming updates and bulk-insert these updates simultaneously.With a reasonable memory overhead (typically only 1 percent of the whole data set), the proposed approach far outperforms theprevious works in terms of update and query performance as well in a realistic environment. In order to further increase buffer hit ratiofor the proposed approach, a new page-replacement policy that exploits the level of buffered node is proposed. Furthermore, weintroduce the concept of deferring threshold ratio (dtr) that simply enables deferring CPU- and I/O-intensive operations such as nodesplits and removals. Extensive experimental evaluation reveals that the proposed approach is far more efficient than previousapproaches for managing frequent updates under various settings.

Journal

  • IEEE transactions on knowledge and data engineering

    IEEE transactions on knowledge and data engineering 21(11), 1573-1589, 2009-11

    IEEE Computer Society

Codes

  • NII Article ID (NAID)
    120001870062
  • NII NACSIS-CAT ID (NCID)
    AA10692959
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    1041-4347
  • Data Source
    IR 
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