Abstract
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.
Partially funded by Fondecyt grant 1-140796, Chile.
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Ferrada, H., Navarro, G. (2014). Efficient Compressed Indexing for Approximate Top-k String Retrieval. In: Moura, E., Crochemore, M. (eds) String Processing and Information Retrieval. SPIRE 2014. Lecture Notes in Computer Science, vol 8799. Springer, Cham. https://doi.org/10.1007/978-3-319-11918-2_3
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DOI: https://doi.org/10.1007/978-3-319-11918-2_3
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