Non-functional requirements (NFRs) play a crucial role in software development also as available choices in decision making procedures for architectural solutions. They often are directly related to design patterns, a powerful method to support the conceptual modeling of system specifications but, due to their complexity and abstraction, they are rarely taken into the proper account in development process. The knowledge on NFRs is usually owned by designers and not formalized in a structured way. We propose a formalization via an ontological language of architectural patterns and non-functional requirements about quality attributes where both the relationships and sequences of patterns and the set non-functional requirements are modelled together with their interactions.

Massive quantities of data are today processed using parallel computing frameworks that parallelize computations on large distributed clusters consisting of many machines. Such frameworks are adopted in big data analytic tasks as recommender systems, social network analysis, legal investigation that involve iterative computations over large datasets. One of the most used framework is MapReduce, scalable and suitable for data-intensive processing with a parallel computation model characterized by sequential and parallel processing interleaving. Its open-source implementation -- Hadoop -- is adopted by many cloud infrastructures as Google, Yahoo, Amazon, Facebook. In this paper we propose a formal approach to model the MapReduce framework using model checking and temporal logics to verify properties of reliability and load balancing of the MapReduce job flow.

Resource retrieval addresses the problem of finding best matches to a request among available resources, with both the request and the resources described with respect to a shared interpretation of the knowledge domain the resource belongs to. The problem of resource matching and retrieval arises in several scenarios, among them, personnel recruitment and job assignment, dating agencies, but also generic electronic marketplaces, Web services discovery and composition, resource matching in the Grid. All these scenarios share a common purpose: given a request, find among available descriptions those best fulfilling it, or at “worse,” when nothing better exists, those that fulfill at least some of the requirements.

Distributed systems are widely employed in nearly all the application domains, but software design for this family of systems still faces a number of challenges. Structured approaches and technologies for addressing and solving these challenges are available but their use is still empirically managed and based on common sense and experience of designers. Design patterns are a meaningful technology for supporting the construction and modeling of software systems. Besides their use is related to the non-functional requirements fulfillment that is also an open challenge in the field of software design. In this work we propose a theoretical approach for modeling relationships and sequences of patterns and for modeling the taxonomy that relates patterns with ensured non-functional requirements for given application contexts. The approach is based on the use of Description Logics for modeling the domain of patterns and for reasoning tasks on the modeled domain. We developed a framework for supporting the architectural modeling phase. Experimental results show the effectiveness of both the patterns conceptualization and the use of non-standard reasoning tasks for solving the problem of matching design patterns satisfying a given set of non-functional requirements with the retrieved subgraph in the pattern ontology.

IgA Nephropathy (IgAN) is a worldwide disease that affects kidneys in human beings and leads to end-stage kidney disease (ESKD) thus requiring renal replacement therapy with dialysis or kidney transplantation. The need for new tools able to help clinicians in predicting ESKD risk for IgAN patients is highly recognized in the medical field. In this paper we present a software tool that exploits the power of artificial neural networks to classify patients’ health status potentially leading to ESKD. The classifier leverages the results returned by an ensemble of 10 networks trained by using data collected in a period of 38 years at University of Bari. The developed tool has been made available both as an online Web application and as an Android mobile app. Noteworthy to its clinical usefulness is that its development is based on the largest available cohort worldwide.

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