24,951 research outputs found
Querying Schemas With Access Restrictions
We study verification of systems whose transitions consist of accesses to a
Web-based data-source. An access is a lookup on a relation within a relational
database, fixing values for a set of positions in the relation. For example, a
transition can represent access to a Web form, where the user is restricted to
filling in values for a particular set of fields. We look at verifying
properties of a schema describing the possible accesses of such a system. We
present a language where one can describe the properties of an access path, and
also specify additional restrictions on accesses that are enforced by the
schema. Our main property language, AccLTL, is based on a first-order extension
of linear-time temporal logic, interpreting access paths as sequences of
relational structures. We also present a lower-level automaton model,
Aautomata, which AccLTL specifications can compile into. We show that AccLTL
and A-automata can express static analysis problems related to "querying with
limited access patterns" that have been studied in the database literature in
the past, such as whether an access is relevant to answering a query, and
whether two queries are equivalent in the accessible data they can return. We
prove decidability and complexity results for several restrictions and variants
of AccLTL, and explain which properties of paths can be expressed in each
restriction.Comment: VLDB201
The combined approach to ontology-based data access
The use of ontologies for accessing data is one of
the most exciting new applications of description
logics in databases and other information systems.
A realistic way of realising sufficiently scalable ontology-
based data access in practice is by reduction
to querying relational databases. In this paper,
we describe the combined approach, which incorporates
the information given by the ontology into
the data and employs query rewriting to eliminate
spurious answers. We illustrate this approach for
ontologies given in the DL-Lite family of description
logics and briefly discuss the results obtained
for the EL family
Private Multiplicative Weights Beyond Linear Queries
A wide variety of fundamental data analyses in machine learning, such as
linear and logistic regression, require minimizing a convex function defined by
the data. Since the data may contain sensitive information about individuals,
and these analyses can leak that sensitive information, it is important to be
able to solve convex minimization in a privacy-preserving way.
A series of recent results show how to accurately solve a single convex
minimization problem in a differentially private manner. However, the same data
is often analyzed repeatedly, and little is known about solving multiple convex
minimization problems with differential privacy. For simpler data analyses,
such as linear queries, there are remarkable differentially private algorithms
such as the private multiplicative weights mechanism (Hardt and Rothblum, FOCS
2010) that accurately answer exponentially many distinct queries. In this work,
we extend these results to the case of convex minimization and show how to give
accurate and differentially private solutions to *exponentially many* convex
minimization problems on a sensitive dataset
Combined FO rewritability for conjunctive query answering in DL-Lite
Standard description logic (DL) reasoning services such as satisfiability and subsumption mainly aim to support TBox design. When the design stage is over and the TBox is used in an actual application, it is usually combined with instance data stored in an ABox, and therefore query answering becomes the most importan
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