Speeding up RDF aggregate discovery through sampling

RDF graphs can be large and complex; finding out interesting information within them is challenging. One easy method for users to discover such graphs is to be shown interesting aggregates (under the form of two-dimensional graphs, i.e., bar charts), where interestingness is evaluated through statistics criteria. Dagger [5] pioneered this approach, however its is quite inefficient, in particular due to the need to evaluate numerous, expensive aggregation queries. In this work, we describe Dagger⁺, which builds upon Dagger and leverages sampling to speed up the evaluation of potentially interesting aggregates. We show that Dagger⁺ achieves very significant execution time reductions, while reaching results very close to those of the original, less efficient system.