We introduce a new family of compressed data structures to efficiently store and query large string dictionaries in main memory. Our main technique is a combination of hierarchical Front-coding with ideas from longest-common-prefix computation in suffix arrays. Our data structures yield relevant space-time tradeoffs in real-world dictionaries. We focus on two domains where string dictionaries are extensively used and efficient compression is required: URL collections, a key element in Web graphs and applications such as Web mining; and collections of URIs and literals, the basic components of RDF datasets. Our experiments show that our data structures achieve better compression than the state-of-the-art alternatives while providing very competitive query times.
CITATION STYLE
Brisaboa, N. R., De Bernardo, G., Cerdeira-Pena, A., & Navarro, G. (2019). Improved compressed string dictionaries. In International Conference on Information and Knowledge Management, Proceedings (pp. 29–38). Association for Computing Machinery. https://doi.org/10.1145/3357384.3357972
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