1 research outputs found
Supporting Hard Queries over Probabilistic Preferences
Preference analysis is widely applied in various domains such as social
choice and e-commerce. A recently proposed framework augments the relational
database with a preference relation that represents uncertain preferences in
the form of statistical ranking models, and provides methods to evaluate
Conjunctive Queries (CQs) that express preferences among item attributes. In
this paper, we explore the evaluation of queries that are more general and
harder to compute.
The main focus of this paper is on a class of CQs that cannot be evaluated by
previous work. These queries are provably hard since relate variables that
represent items being compared. To overcome this hardness, we instantiate these
variables with their domain values, rewrite hard CQs as unions of such
instantiated queries, and develop several exact and approximate solvers to
evaluate these unions of queries. We demonstrate that exact solvers that target
specific common kinds of queries are far more efficient than general solvers.
Further, we demonstrate that sophisticated approximate solvers making use of
importance sampling can be orders of magnitude more efficient than exact
solvers, while showing good accuracy. In addition to supporting provably hard
CQs, we also present methods to evaluate an important family of count queries,
and of top-k queries.Comment: This is the technical report of the following paper: Supporting Hard
Queries over Probabilistic Preferences. PVLDB, 13(7): 1134-1146, 2019. DOI:
https://doi.org/10.14778/3384345.338435