6,167 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
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
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
Discovery Agent : An Interactive Approach for the Discovery of Inclusion Dependencies
The information integration problem is a hard yet important problem in the field of databases. The goal of information integration is to provide unified views on diverse data among several resources. This subject has been studied for a long time. The integration can be performed using several ways. Schema integration using inclusion dependency constraints is one of them. The problem of discovering inclusion dependencies among input relations is NP-complete in terms of the number of attributes.
Two significant algorithms address this problem: FIND2 by Andreas Koeller and Zigzag by Fabien De Marchi. Both algorithms discover inclusion dependencies among input relations on small scale databases having relatively few attributes. Because of the data discrepancy, they do not scale well with higher numbers of attributes.
We propose an approach of incorporating human intelligence into the algorithmic discovery of inclusion dependencies. To use human intelligence, we design an agent called the discovery agent, to provide a communication bridge between an algorithm and a user. The discovery agent demonstrates the progress of the discovery process and provides sufficient user controls to govern the discovery process into the right direction. In this thesis, we present a prototype of the discovery agent based upon the FIND2 algorithm, which utilizes most of the phase-wise behavior of the algorithm and demonstrate how human observer and algorithm work together to achieve higher performance and better output accuracy.
The goal of the discovery agent is to make the discovery process truly interactive between system and user as well as to produce the desired and accurate result. The discovery agent can deliver an applicable and feasible approximation of an NP-complete problem with the help of suitable algorithm and appropriate human expertise
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