10,373 research outputs found
Querying Incomplete Data : Complexity and Tractability via Datalog and First-Order Rewritings
To answer database queries over incomplete data the gold standard is finding
certain answers: those that are true regardless of how incomplete data is
interpreted. Such answers can be found efficiently for conjunctive queries and
their unions, even in the presence of constraints. With negation added, the
problem becomes intractable however. We concentrate on the complexity of
certain answers under constraints, and on effficiently answering queries
outside the usual classes of (unions) of conjunctive queries by means of
rewriting as Datalog and first-order queries. We first notice that there are
three different ways in which query answering can be cast as a decision
problem. We complete the existing picture and provide precise complexity bounds
on all versions of the decision problem, for certain and best answers. We then
study a well-behaved class of queries that extends unions of conjunctive
queries with a mild form of negation. We show that for them, certain answers
can be expressed in Datalog with negation, even in the presence of functional
dependencies, thus making them tractable in data complexity. We show that in
general Datalog cannot be replaced by first-order logic, but without
constraints such a rewriting can be done in first-order. The paper is under
consideration in Theory and Practice of Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
Structurally Tractable Uncertain Data
Many data management applications must deal with data which is uncertain,
incomplete, or noisy. However, on existing uncertain data representations, we
cannot tractably perform the important query evaluation tasks of determining
query possibility, certainty, or probability: these problems are hard on
arbitrary uncertain input instances. We thus ask whether we could restrict the
structure of uncertain data so as to guarantee the tractability of exact query
evaluation. We present our tractability results for tree and tree-like
uncertain data, and a vision for probabilistic rule reasoning. We also study
uncertainty about order, proposing a suitable representation, and study
uncertain data conditioned by additional observations.Comment: 11 pages, 1 figure, 1 table. To appear in SIGMOD/PODS PhD Symposium
201
Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
Causality has been recently introduced in databases, to model, characterize
and possibly compute causes for query results (answers). Connections between
query causality and consistency-based diagnosis and database repairs (wrt.
integrity constrain violations) have been established in the literature. In
this work we establish connections between query causality and abductive
diagnosis and the view-update problem. The unveiled relationships allow us to
obtain new complexity results for query causality -the main focus of our work-
and also for the two other areas.Comment: To appear in Proc. UAI Causal Inference Workshop, 2015. One example
was fixe
Tractable Optimization Problems through Hypergraph-Based Structural Restrictions
Several variants of the Constraint Satisfaction Problem have been proposed
and investigated in the literature for modelling those scenarios where
solutions are associated with some given costs. Within these frameworks
computing an optimal solution is an NP-hard problem in general; yet, when
restricted over classes of instances whose constraint interactions can be
modelled via (nearly-)acyclic graphs, this problem is known to be solvable in
polynomial time. In this paper, larger classes of tractable instances are
singled out, by discussing solution approaches based on exploiting hypergraph
acyclicity and, more generally, structural decomposition methods, such as
(hyper)tree decompositions
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