Index Tuning for Efficient Proximity-Enhanced Query Processing

  • Andreas Broschart
  • Ralf Schenkel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6203)

Abstract

Scoring models that make use of proximity information usually improve result quality in text retrieval. Considering that index structures carrying proximity information can grow huge in size if they are not pruned, it is helpful to tune indexes towards space requirements and retrieval quality. This paper elaborates on our approach used for INEX 2009 to tune index structures for different choices of result size k. Our best tuned index structures provide the best CPU times for type A queries among the Efficiency Track participants, still providing at least BM25 retrieval quality. Due to the number of query terms, Type B queries cannot be processed equally performant. To allow for comparison as to retrieval quality with non-pruned index structures, we also depict our results from the Adhoc Track.

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References

  1. 1.
    Broschart, A., Schenkel, R., Theobald, M.: Experiments with proximity-aware scoring for xml retrieval at inex 2008. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2008. LNCS, vol. 5631, pp. 29–32. Springer, Heidelberg (2009)CrossRefGoogle Scholar
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    Büttcher, S., Clarke, C.L.A., Lushman, B.: Term proximity scoring for ad-hoc retrieval on very large text collections. In: SIGIR, pp. 621–622 (2006)Google Scholar
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    Schenkel, R., Broschart, A., won Hwang, S., Theobald, M., Weikum, G.: Efficient text proximity search. In: Ziviani, N., Baeza-Yates, R.A. (eds.) SPIRE 2007. LNCS, vol. 4726, pp. 287–299. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andreas Broschart
    • 1
    • 2
  • Ralf Schenkel
    • 1
    • 2
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.Saarland UniversitySaarbrückenGermany

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