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Efficient Exploration of Interesting Aggregates in RDF Graphs

Yanlei Diao 1, 2 Pawel Guzewicz 1, 2 Ioana Manolescu 1, 2 Mirjana Mazuran 1, 2 
2 CEDAR - Rich Data Analytics at Cloud Scale
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France
Abstract : As large Open Data are increasingly shared as RDF graphs today, there is a growing demand to help users discover the most interesting facets of a graph, which are often hard to grasp without automatic tools. We consider the problem of automatically identifying the k most interesting aggregate queries that can be evaluated on an RDF graph, given an integer k and a user-specified interestingness function. Our problem departs from analytics in relational data warehouses in that (i) in an RDF graph we are not given but we must identify the facts, dimensions, and measures of candidate aggregates; (ii) the classical approach to efficiently evaluating multiple aggregates breaks in the face of multi-valued dimensions in RDF data. In this work, we propose an extensible end-to-end framework that enables the identification and evaluation of interesting aggregates based on a new RDF-compatible one-pass algorithm for efficiently evaluating a lattice of aggregates and a novel early-stop technique (with probabilistic guarantees) that can prune uninteresting aggregates. Experiments using both real and synthetic graphs demonstrate the ability of our framework to find interesting aggregates in a large search space, the efficiency of our algorithms (with up to 2.9× speedup over a similar pipeline based on existing algorithms), and scalability as the data size and complexity grow.
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Submitted on : Monday, August 16, 2021 - 4:47:09 PM
Last modification on : Wednesday, April 6, 2022 - 3:48:49 PM
Long-term archiving on: : Wednesday, November 17, 2021 - 6:34:21 PM


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Yanlei Diao, Pawel Guzewicz, Ioana Manolescu, Mirjana Mazuran. Efficient Exploration of Interesting Aggregates in RDF Graphs. SIGMOD/PODS '21 - International Conference on Management of Data, Jun 2021, Virtual Event China, China. pp.392-404, ⟨10.1145/3448016.3457307⟩. ⟨hal-03320929⟩



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