
The use of dictionaries is a common practice among those applications performing on huge RDF datasets. It allows long terms occurring in the RDF triples to be replaced by short IDs which reference them. This decision greatly compacts the dataset and thus mitigates its scalability issues. However, the dictionary size is not negligible and the techniques used for its representation also suffer from scalability limitations. This paper focuses on this scenario by adapting compression techniques for string dictionaries to the case of RDF. We propose a novel technique: Dcomp, which can be tuned to represent the dictionary in compressed space (22--64%) and to perform in a few microseconds (1--50μs).
502050 Wirtschaftsinformatik, 102001 Artificial intelligence, 102001 Artificial Intelligence, 102015 Information systems, 502050 Business informatics, 102015 Informationssysteme, 102
502050 Wirtschaftsinformatik, 102001 Artificial intelligence, 102001 Artificial Intelligence, 102015 Information systems, 502050 Business informatics, 102015 Informationssysteme, 102
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| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
