Knowledge and Information Systems

, Volume 10, Issue 2, pp 211–227

Extending metric index structures for efficient range query processing

  • Karin Kailing
  • Hans-Peter Kriegel
  • Martin Pfeifle
  • Stefan Schönauer
Short Paper

DOI: 10.1007/s10115-006-0018-6

Cite this article as:
Kailing, K., Kriegel, HP., Pfeifle, M. et al. Knowl Inf Syst (2006) 10: 211. doi:10.1007/s10115-006-0018-6

Abstract

Databases are getting more and more important for storing complex objects from scientific, engineering, or multimedia applications. Examples for such data are chemical compounds, CAD drawings, or XML data. The efficient search for similar objects in such databases is a key feature. However, the general problem of many similarity measures for complex objects is their computational complexity, which makes them unusable for large databases. In this paper, we combine and extend the two techniques of metric index structures and multi-step query processing to improve the performance of range query processing. The efficiency of our methods is demonstrated in extensive experiments on real-world data including graphs, trees, and vector sets.

Keywords

Complex objects Metric indexing Multi-step query processing Density-based clustering 

Copyright information

© Springer-Verlag London Ltd 2006

Authors and Affiliations

  • Karin Kailing
    • 1
  • Hans-Peter Kriegel
    • 1
  • Martin Pfeifle
    • 1
  • Stefan Schönauer
    • 1
  1. 1.Institute for Computer ScienceUniversity of MunichMunichGermany

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