- Working as Researcher in the EU H2020 projects:
Worked as lecturer in Department of Computer Science and Engineering. Conducted research in the area of business analysis and service design.
Worked as software engineer in a ERP system backend design.
BRAIN-IoT focuses on complex scenarios where actuation and control are cooperatively supported by populations of IoT systems. The breakthrough targeted by BRAIN-IoT is to establish a framework and methodology supporting smart cooperative behaviour in fully de-centralized, composable and dynamic federations of heterogeneous IoT platforms.
BRAIN-IoT tackles future business-critical and privacy-sensitive IoT scenarios subject to strict dependability requirements. In this complex setting, BRAIN-IoT enables smart autonomous behaviour in IoT scenarios involving heterogeneous sensors and actuators autonomously cooperating in complex, dynamic tasks. This is done by employing highly dynamic federations of heterogeneous IoT platforms able to support secure and scalable operations for future IoT use cases, backed by an open decentralized marketplace of IoT platform and smart features, supporting runtime deployment and reconfiguration.
Open semantic models are used to enforce interoperable operations and exchange of data and control features, supported by model-based development tools to ease prototyping and integration of interoperable solutions. Overall, secure operations are guaranteed by a consistent framework providing AAA features in highly dynamic, distributed IoT scenarios, joint with solutions to embed privacy awareness and control features. The viability of the proposed approaches is demonstrated in two futuristic usage scenarios, namely Service Robotics and Critical Infrastructure Management, as well as through a series of proof-of-concept demonstrations in collaboration with on-going IoT large-scale pilot initiatives.
MONSOON is a EU SPIRE project that joins 11 complementary partners from 7 different European Countries - Italy, Germany, Greece, Slovakia, France, Portugal and Spain (Madrid). All partners are combining knowledge to achieve project goals.
The MONSOON vision is to provide Process Industries with dependable tools to help achieving improvements in the efficient use and re-use of raw resources and energy.
MONSOON aims at establishing a data-driven methodology supporting the exploitation of optimization potentials by applying multi-scale model based predictive controls in production processes.
MONSOON features harmonized site-wide dynamic models and builds upon the concept of the cross-sectorial data lab, a collaborative environment where high amounts of data from multiple sites are collected and processed in a scalable way. The data lab enables multidisciplinary collaboration of experts allowing teams to jointly model, develop and evaluate distributed controls in rapid and cost-effective way. Hybrid simulation and seamless integration techniques are adopted for rapid prototyping and deployment in real conditions.
Requirements of modern production processes stress the need of greater agility and flexibility leading to faster production cycles, increased productivity, less waste and more sustainable production.
The goal of COMPOSITION is to develop an integrated information management system (IIMS) which optimises the internal production processes by exploiting existing data, knowledge and tools to increase productivity and dynamically adapt to changing market requirements.
The project will also develop an ecosystem to support the interchange of data and services between factories and their suppliers with the aim to invite new market actors into the supply chain.
In recent years, numerous efforts have been put towards sharing Knowledge Bases (KB) in the Linked Open Data (LOD) cloud. These KBs are being used for various tasks, including performing data analytics or building question answering systems. Such KBs evolve: their data (instances) and schemas can be updated, extended, revised and refactored. However, unlike in more controlled types of knowledge bases, the evolution of KBs exposed in the LOD cloud is usually unrestrained, what may cause data to suffer from a variety of quality issues, both at a semantic (contradiction) and at a pragmatic level (ambiguity, inaccuracies). This situation affects negatively data stakeholders – consumers, curators, etc. –. Data quality is commonly related to the perception of the fitness for use, for a certain application or use case. Therefore, ensuring the quality of the data of a knowledge base that evolves is vital. Since data is derived from autonomous, evolving, and increasingly large data providers, it is impractical to do manual data curation, and at the same time, it is very challenging to do a continuous automatic assessment of data quality. Ensuring the quality of a KB is a non-trivial task since they are based on a combination of structured information supported by models, ontologies, and vocabularies, as well as queryable endpoints, links, and mappings. Thus, in this project, we explored two main areas in assessing KB quality: (i) quality assessment using KB evolution analysis, and (ii) validation using machine learning models.
The technological development of the last decades made it possible to accumulate a large amount of data on every aspect of our public or private life. Not many of us know that a large part of those data are publicly available: several public administrations are already publishing large data sets, that citizen could use to generate innovative applications to change the way we live, move, use the city and the territory. There is a clear gap between the opportunities offered by the abundance of open data and the citizens’ capability to imagine new ways of using such data. O4Ctextworks to reduce such gap. It involves citizens into a co-design process (hackathons), together with IT experts, public administrations, interest groups and start-up companies, in order to develop new services to improve urban quality and certain aspects of their everyday life. The aim of the project is to raise citizens’ awareness about the opportunity offered by open data and create a new culture of innovation in public services. In each of the five pilot locations (Copenhagen, Karlstad, Rotterdam, Milano and Barcelona) the project will also create physical or virtual locations (OpenDataLab) that will become the reference point for all citizens and interest groups that want to propose innovative applications based on open data.
Il progetto di ricerca “IRMA (Integrated Real-time Mobility Assistant)”, nato nel 2011 dalla sperimentazione di piattaforme software a supporto del viaggiatore e del pendolare, ha previsto due iniziative complementari: il progetto per Pavia e la proposta di progetto europeo per la call 7.1 “Intelligent Transport Systems – Connectivity and information sharing for intelligent mobility” del programma europeo di ricerca Horizon 2020. Il Dipartimento di Ingegneria Industriale e della Informazione dell’Università di Pavia ha sviluppato il prototipo del progetto, mentre il Comune di Pavia ha collaborato mettendo a disposizione i dati sui trasporti ed adeguando le proprio procedure alla collaborazione con i cittadini, di intesa con la azienda di trasporti Line.