CINI (Consorzio Interuniversitario Nazionale per l’Informatica) is the main reference for the national research in Computer Science and Information Technology. CINI is a consortium founded in the 1989 by nine universities with headquarters in Rome and under the supervision of the Ministry of University and Research. At present, 36 universities participate to the consortium. CINI is organised in local operating units and in national research areas covering all the different aspects of IT. At present, it includes 1,278 professors and researchers in Computer Science related areas. It is distributed across the entire country, the headquarters are in Rome. CINI promotes and coordinates: frontier and industrial research in Computer Science, technology transfer and scientific activities in collaboration with universities, research centres, and companies.
Relevant Expertise for the project:
CINI is a very active organisation at National and EU level. It participates to several national and international projects. At present, there are projects funded by several entities including: EU (ARTEMIS, FP6, and FP7), Italian Ministry of University, Italian Public Administrations, private companies, etc. It participates to the planning of national research activities (e.g., PNR 2011-2013) and to European research initiatives in ITC (e.g., the NESSI technology platform). It provides support to the associations of university professors and PhD schools. It collaborates with companies in applied research activities and technology transfer in the ICT area.
Role in the project:
Systems reliability is always difficult to estimate properly due to a number of factors that can affect the results of the predictive models used. Therefore, even if such models are important for a proper planning, they are not enough and they can be enhanced using up-to-date and continuous information flows that can be collected from the overall system at run-time. Such additional knowledge can be used to tune continuously ad-hoc models to enhance their prediction capabilities. Moreover, in a connected world, such information can also be shared with similar systems deployed in comparable environments to further improve the prediction abilities based on a larger set of data. However, the definition of reliable models cannot be done only on the base of theories, it require a continuous experimentation process to verify the effectiveness of the model in real conditions and a related tuning.
The contribution of CINI will focus on the data analysis of information flows coming from different sources to take proper decisions at different times (WP4):
At design time: use already available information from existing systems to create predictive models that can be adapted and used to support the development and/or the maintenance of a systems.
- At run-time: use of on-line data collection and analysis techniques to make short-term and longterm predictions on the status of a working system to plan maintenance properly. Such models will be adaptive, they will be able to learn from the operating conditions of the single system under investigation and from other similar systems deployed in similar conditions. Moreover, CINI will contribute to the development of the reference framework (WP2) integrating the abilities described and supporting the definition of common application scenarios.