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Title | Ranked Document Retrieval in (Almost) No Space |
Authors | Nieves Brisaboa, Ana Cerdeira, Gonzalo Navarro, Óscar Pedreira Fernández |
Publication date | 2012 |
Abstract | Ranked document retrieval is a fundamental task in search engines. Such queries are solved with inverted indices that require additional 45%-80% of the compressed text space, and take tens to hundreds of microseconds per query. In this paper we show how ranked document retrieval queries can be solved within tens of milliseconds using essentially no extra space over an in-memory compressed representation of the document collection. More precisely, we enhance wavelet trees on bytecodes (WTBCs), a data structure that rearranges the bytes of the compressed collection, so that they support ranked conjunctive and disjunctive queries, using just 6%-18% of the compressed text space. |
Downloaded | 12 times |
Pages | 155-160 |
Conference name | International Symposium on String Processing and Information Retrieval |
Publisher | Springer-Verlag (Berlin/Heidelberg, Germany) |
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