  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 2.9x speedup over a similar pipeline
based on existing algorithms), and scalability as the data size and complexity
grow.
