Current mobile systems for assisted navigation have limited effectiveness in satisfying user needs. The information content supporting location-based service discovery and path calculation is usually limited. Semantic-based technologies can allow to overcome these limitations, by exploiting accurate and meaningful descriptions of locations, POIs, road segments and environmental conditions. We present here a general framework leveraging an enriched cartography, which may be useful not only to enhance travel satisfaction and safety, but also to regulate vehicle efficiency, traffic and environmental impact. The availability of semantically annotated map data is a crucial requirement for this kind of proposals to be practically viable. Unfortunately, the majority of available systems is developed upon closed and proprietary solutions for both maps and software applications, so third parties cannot extend their functionality. In order to overcome this restriction, the framework proposed here is based on open standards and tools: in particular, it leverages Semantic Web technologies and crowd-sourced maps available from OpenStreetMap (http://www.openstreetmap.org/), enriching nodes and POIs with semantic annotations to enable innovative Location Based Services (LBSs) for traveling users. Particularly, the paper proposes a general method for storing semantic annotations into OpenStreetMap road nodes and POIs, expressed w.r.t. ontologies in standard Semantic Web languages. A user-friendly software tool is also presented for editing semantic annotations through a fully visual user interface, based on simple drag-and-drop operations, implemented as a plugin for the popular open source JOSM Open-StreetMap editor (http://josm.openstreetmap.de/), that will make any OpenStreetMap contributor capable of enriching maps with semantic information. Finally, a semantic-enhanced navigation tool is proposed, capable of exploiting the enriched OpenStreetMap cartography. We present the framework, the tool, and an initial users’ evaluation.

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.

Competence management calls for automated knowledge-based services in order to take full advantage from the know-how of a company. This paper presents an integrated semantic-based knowledge management system providing decision support services for several activities typical of competence management, including core competence evaluation, human resources allocation, training programs planning. Knowledge resources are represented according to the formalism of Description Logics, which also allows for inference services crucial for the implemented solutions. We present the various features of our approach, which exploit advanced non-standard reasoning services from Description Logics, specifically developed to support knowledge management.

Reasoning engines are largely used in resource discovery and matchmaking scenarios where, given a request, they are able to provide a list of compatible items arranged in relevance order. A significant added value is the possibility to explain match outcomes in order to obtain information for modifying or refining early queries. Though the feasibility of running logic-based reasoning tasks over various knowledge bases has been widely proven on fixed servers, it is a challenging subject to execute inference processes on handheld devices. The paper presents a revised lightweight version of abduction and contraction algorithms (going back to previous works) for matchmaking in Description Logics in mobile ad-hoc contexts. Implementation and tests have been carried out in a mobile P2P case study based on a simplified Bluetooth interaction paradigm.

Home and building automation (HBA) trends toward the Ambient Intelligence paradigm, which aims to autonomously coordinate and control appliances and subsystems in a given environment. Nevertheless, HBA is based on an explicit user–home interaction and basically enables static and predetermined scenarios. This paper proposes a more flexible multi-agent approach, leveraging semantic-based resource discovery and orchestration for HBA applications. Backward-compatible enhancements to EIB/KNX domotic standard allow to support the semantic characterization of user profiles and device functionalities, thus enabling: 1) negotiation of the most suitable home services/functionalities according to implicit and explicit user needs and 2) device-driven interaction for adapting the environment to context evolution. A power-management problem in HBA is presented as a case study to better clarify the proposal and assess its effectiveness.