  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.
