Speeding up RDF aggregate discovery through sampling

Ioana Manolescu 1 Mirjana Mazuran 1
1 CEDAR - Rich Data Analytics at Cloud Scale
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
Abstract : 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 (un-der 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.
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https://hal.inria.fr/hal-02065993
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Submitted on : Tuesday, March 19, 2019 - 5:03:35 PM
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Ioana Manolescu, Mirjana Mazuran. Speeding up RDF aggregate discovery through sampling. 2nd International Workshop on Big Data Visual Exploration and Analytics, 2019, Mar 2019, Lisbon, Portugal. ⟨hal-02065993⟩

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