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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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Submitted on : Tuesday, March 19, 2019 - 5:03:35 PM
Last modification on : Friday, April 30, 2021 - 10:04:40 AM
Long-term archiving on: : Thursday, June 20, 2019 - 12:49:58 PM


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  • HAL Id : hal-02065993, version 1



Ioana Manolescu, Mirjana Mazuran. Speeding up RDF aggregate discovery through sampling. BigVis 2019 - 2nd International Workshop on Big Data Visual Exploration and Analytics, Mar 2019, Lisbon, Portugal. ⟨hal-02065993⟩



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