1,016 research outputs found
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
Logics for Unranked Trees: An Overview
Labeled unranked trees are used as a model of XML documents, and logical
languages for them have been studied actively over the past several years. Such
logics have different purposes: some are better suited for extracting data,
some for expressing navigational properties, and some make it easy to relate
complex properties of trees to the existence of tree automata for those
properties. Furthermore, logics differ significantly in their model-checking
properties, their automata models, and their behavior on ordered and unordered
trees. In this paper we present a survey of logics for unranked trees
Composition and Inversion of Schema Mappings
In the recent years, a lot of attention has been paid to the development of
solid foundations for the composition and inversion of schema mappings. In this
paper, we review the proposals for the semantics of these crucial operators.
For each of these proposals, we concentrate on the three following problems:
the definition of the semantics of the operator, the language needed to express
the operator, and the algorithmic issues associated to the problem of computing
the operator. It should be pointed out that we primarily consider the
formalization of schema mappings introduced in the work on data exchange. In
particular, when studying the problem of computing the composition and inverse
of a schema mapping, we will be mostly interested in computing these operators
for mappings specified by source-to-target tuple-generating dependencies
Oblivious Bounds on the Probability of Boolean Functions
This paper develops upper and lower bounds for the probability of Boolean
functions by treating multiple occurrences of variables as independent and
assigning them new individual probabilities. We call this approach dissociation
and give an exact characterization of optimal oblivious bounds, i.e. when the
new probabilities are chosen independent of the probabilities of all other
variables. Our motivation comes from the weighted model counting problem (or,
equivalently, the problem of computing the probability of a Boolean function),
which is #P-hard in general. By performing several dissociations, one can
transform a Boolean formula whose probability is difficult to compute, into one
whose probability is easy to compute, and which is guaranteed to provide an
upper or lower bound on the probability of the original formula by choosing
appropriate probabilities for the dissociated variables. Our new bounds shed
light on the connection between previous relaxation-based and model-based
approximations and unify them as concrete choices in a larger design space. We
also show how our theory allows a standard relational database management
system (DBMS) to both upper and lower bound hard probabilistic queries in
guaranteed polynomial time.Comment: 34 pages, 14 figures, supersedes: http://arxiv.org/abs/1105.281
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