Stream processing has inspired new computational approaches to facilitate effectiveness and efficiency. One such approach is the dynamic pipeline, which serves as a powerful computational model for stream processing. It is particularly well suited for solving problems that require incremental generation of results, making it an approach for scenarios where real-time analysis and responsiveness are critical. This paper aims to address a family of problems using the Dynamic Pipeline approach, and as a first step, we provide a comprehensive characterization of this problem family. In addition, we present the definition of a Dynamic Pipeline framework. To demonstrate the practicality of this framework, we present a proof of concept through its implementation and perform an empirical performance study. To this end, we focus on solving the problem of enumerating or listing the weakly connected components of a graph within the proposed framework. We provide two implementations of this algorithm to demonstrate the computational power and continuous behavior of the Dynamic Pipeline framework. The first implementation serves as a baseline for our experiments, representing an ad hoc solution based on the Dynamic Pipeline approach. In contrast, the second implementation is built on top of the developed framework. The observed results strongly support the suitability and effectiveness of the Dynamic Pipeline framework for implementing graph stream processing problems, especially those where continuous and real-time result generation is essential.

Graph databases are now playing an important role because they allow us to overcome some limitations of relational databases. In particular, in graph databases we are interested not only on the data contained but also on its topology. As a consequence, most graph database queries are navigational, asking whether some nodes are connected by edges or paths. Up to now, most foundational work has concentrated on the study of computational models and query languages, analyzing their expressivity, computability, and complexity. However, in our work we address a different kind of foundational work. We are not concerned with expressibility, efficiency or feasibility issues, but with correctness. More precisely, given an algorithm or an implementation for solving queries, how can we be sure that the answers obtained are correct (soundness) and that all possible correct answers are obtained by our implementation (completeness). In this sense, in this paper we first present a core query language, similar to Cypher or G-Core. Then, we define a simple logic whose formulas are precisely the database queries, and whose satisfaction relation defines what is a correct answer. Finally, we define an operational semantics, which could be seen as an abstract implementation of our language, showing that the semantics is correct, i.e. sound and complete with respect to our logic.

Relationships like friendship to limit access to resources have been part of social network applications since their beginnings. Describing access control policies in terms of relationships is not particular to social networks and it arises naturally in many situations. Hence, we have recently seen several proposals formalizing different Relationship-based Access Control (ReBAC) models. In this paper, we introduce a class of Datalog programs suitable for modeling ReBAC and argue that this class of programs, that we called ReBAC Datalog policies, provides a very general framework to specify and implement ReBAC policies. To support our claim, we first formalize the merging of two recent proposals for modeling ReBAC, one based on hybrid logic and the other one based on path regular expressions. We present extensions to handle negative authorizations and temporal policies. We describe mechanism for policy analysis, and then discuss the feasibility of using Datalog-based systems as implementations.

In this paper we show that the logical framework proposed by Becker et al. [1] to reason about security policy behavior in a trust management context can be captured by an operational framework that is based on the language proposed by Miller in 1989 to deal with scoping and/or modules in logic programming. The framework of Becker et al. uses propositional Horn clauses to represent both policies and credentials, implications in clauses are interpreted in counterfactual logic, a Hilbert-style proof system is defined and a system based on SAT is used to prove whether properties about credentials, permissions and policies are valid, i.e. true under all possible policies. Our contributions in this paper are three. First, we show that this kind of validation can rely on an operational semantics (derivability relation) of a language very similar to Miller's language, which is very close to derivability in logic programs. Second, we are able to establish that, as in propositional logic, validity of formulas is a co-NP-complete problem. And third, we present a provably correct implementation of a goal-oriented algorithm for validity.