Efficient Compressed Indexing for Approximate Top-k String Retrieval

  • Héctor Ferrada
  • Gonzalo Navarro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8799)


Given a collection of strings (called documents), the top-k document retrieval problem is that of, given a string pattern p, finding the k documents where p appears most often. This is a basic task in most information retrieval scenarios. The best current implementations require 20–30 bits per character (bpc) and k to 4k microseconds per query, or 12–24 bpc and 1–10 milliseconds per query. We introduce a Lempel-Ziv compressed data structure that occupies 5–10 bpc to answer queries in around k microseconds. The drawback is that the answer is approximate, but we show that its quality improves asymptotically with the size of the collection, reaching over 85% of the accumulated term frequency of the real answer already for patterns of length 4–6 on rather small collections, and improving for larger ones.


Compressed Indexing Pattern Length Empty Node Short Pattern Large Text Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Héctor Ferrada
    • 1
  • Gonzalo Navarro
    • 1
  1. 1.Department of Computer ScienceUniversity of ChileChile

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