Compositions of ternary relations

summary:In this paper, we introduce six basic types of composition of ternary relations, four of which are associative. These compositions are based on two types of composition of a ternary relation with a binary relation recently introduced by Zedam et al. We study the properties of these compositions, in particular the link with the usual composition of binary relations through the use of the operations of projection and cylindrical extension

Information Flow Model for Commercial Security

Information flow in Discretionary Access Control (DAC) is a well-known difficult problem. This paper formalizes the fundamental concepts and establishes a theory of information flow security. A DAC system is information flow secure (IFS), if any data never flows into the hands of owner’s enemies (explicitly denial access list.

On generalized fuzzy relational equations and their applications

AbstractThe paper provides an idea of generalization of fuzzy relational equations where t- and s-norms are introduced. The first part contains an extensive presentation of the resolution of fuzzy relational equations; next the solutions are specified for a list of several triangular norms. Moreover the dual equations are considered. The second part deals with the applicational aspects of these equations in systems analysis, decision-making, and arithmetic of fuzzy numbers

Fuzzy Galois connections on fuzzy sets

In fairly elementary terms this paper presents how the theory of preordered fuzzy sets, more precisely quantale-valued preorders on quantale-valued fuzzy sets, is established under the guidance of enriched category theory. Motivated by several key results from the theory of quantaloid-enriched categories, this paper develops all needed ingredients purely in order-theoretic languages for the readership of fuzzy set theorists, with particular attention paid to fuzzy Galois connections between preordered fuzzy sets.Comment: 30 pages, final versio

Infinite Probabilistic Databases

Probabilistic databases (PDBs) are used to model uncertainty in data in a quantitative way. In the standard formal framework, PDBs are finite probability spaces over relational database instances. It has been argued convincingly that this is not compatible with an open-world semantics (Ceylan et al., KR 2016) and with application scenarios that are modeled by continuous probability distributions (Dalvi et al., CACM 2009). We recently introduced a model of PDBs as infinite probability spaces that addresses these issues (Grohe and Lindner, PODS 2019). While that work was mainly concerned with countably infinite probability spaces, our focus here is on uncountable spaces. Such an extension is necessary to model typical continuous probability distributions that appear in many applications. However, an extension beyond countable probability spaces raises nontrivial foundational issues concerned with the measurability of events and queries and ultimately with the question whether queries have a well-defined semantics. It turns out that so-called finite point processes are the appropriate model from probability theory for dealing with probabilistic databases. This model allows us to construct suitable (uncountable) probability spaces of database instances in a systematic way. Our main technical results are measurability statements for relational algebra queries as well as aggregate queries and Datalog queries