125,834 research outputs found
Differential Privacy for Relational Algebra: Improving the Sensitivity Bounds via Constraint Systems
Differential privacy is a modern approach in privacy-preserving data analysis
to control the amount of information that can be inferred about an individual
by querying a database. The most common techniques are based on the
introduction of probabilistic noise, often defined as a Laplacian parametric on
the sensitivity of the query. In order to maximize the utility of the query, it
is crucial to estimate the sensitivity as precisely as possible.
In this paper we consider relational algebra, the classical language for
queries in relational databases, and we propose a method for computing a bound
on the sensitivity of queries in an intuitive and compositional way. We use
constraint-based techniques to accumulate the information on the possible
values for attributes provided by the various components of the query, thus
making it possible to compute tight bounds on the sensitivity.Comment: In Proceedings QAPL 2012, arXiv:1207.055
Relational Expressions for Data Transformation and Computation
Separate programming models for data transformation (declarative) and
computation (procedural) impact programmer ergonomics, code reusability and
database efficiency. To eliminate the necessity for two models or paradigms, we
propose a small but high-leverage innovation: the introduction of complete
relations into the relational database. Complete relations and the discipline
of constraint programming, which concerns them, are founded on the same algebra
as relational databases. We claim that by synthesising the relational database
of Codd and Date, with the results of the constraint programming community, the
relational model holistically offers programmers a single declarative paradigm
for both data transformation and computation, reusable code with computations
that are indifferent to what is input and what is output, and efficient
applications with the query engine optimising and parallelising all levels of
data transformation and computation.Comment: 12 pages, 4 tables. To be published in the proceedings of the
Shepherding Track of the 2023 Australasian Database Conference Melbourne (Nov
1-3
Towards Intelligent Databases
This article is a presentation of the objectives and techniques
of deductive databases. The deductive approach to databases aims at extending
with intensional definitions other database paradigms that describe
applications extensionaUy. We first show how constructive specifications can
be expressed with deduction rules, and how normative conditions can be defined
using integrity constraints. We outline the principles of bottom-up and
top-down query answering procedures and present the techniques used for
integrity checking. We then argue that it is often desirable to manage with
a database system not only database applications, but also specifications of
system components. We present such meta-level specifications and discuss
their advantages over conventional approaches
A theorem prover-based analysis tool for object-oriented databases
We present a theorem-prover based analysis tool for object-oriented database systems with integrity constraints. Object-oriented database specifications are mapped to higher-order logic (HOL). This allows us to reason about the semantics of database operations using a mechanical theorem prover such as Isabelle or PVS. The tool can be used to verify various semantics requirements of the schema (such as transaction safety, compensation, and commutativity) to support the advanced transaction models used in workflow and cooperative work. We give an example of method safety analysis for the generic structure editing operations of a cooperative authoring system
Constrained Query Answering
Traditional answering methods evaluate queries only against positive
and definite knowledge expressed by means of facts and deduction rules. They do
not make use of negative, disjunctive or existential information. Negative or indefinite
knowledge is however often available in knowledge base systems, either as
design requirements, or as observed properties. Such knowledge can serve to rule out
unproductive subexpressions during query answering. In this article, we propose an
approach for constraining any conventional query answering procedure with general,
possibly negative or indefinite formulas, so as to discard impossible cases and to
avoid redundant evaluations. This approach does not impose additional conditions
on the positive and definite knowledge, nor does it assume any particular semantics
for negation. It adopts that of the conventional query answering procedure it
constrains. This is achieved by relying on meta-interpretation for specifying the
constraining process. The soundness, completeness, and termination of the underlying
query answering procedure are not compromised. Constrained query answering
can be applied for answering queries more efficiently as well as for generating more
informative, intensional answers
Integrating Datalog and Constraint Solving
LP is a common formalism for the field of databases and CSP, both at the
theoretical level and the implementation level in the form of Datalog and CLP.
In the past, close correspondences have been made between both fields at the
theoretical level. Yet correspondence at the implementation level has been much
less explored. In this article we work towards relating them at the
implementation level. Concretely, we show how to derive the efficient Leapfrog
Triejoin execution algorithm of Datalog from a generic CP execution scheme.Comment: Proceedings of the 13th International Colloquium on Implementation of
Constraint LOgic Programming Systems (CICLOPS 2013), Istanbul, Turkey, August
25, 201
- …