Section: Computer Science and Engineering
Tutor: PRADELLA MATTEO
Advisor: ROSSI MATTEO GIOVANNI Major Research topic
:EFFICIENT TECHNIQUES TO AUTOMATICALLY LEARN SEMANTIC MAPPINGS FOR THE CONVERSION OF MESSAGES BETWEEN HETEROGENOUS FORMATSAbstract:
The modern vision of transportation is that of "mobility as a service", in which users can seamlessly build door-to-door trips including several travel modes through a single entry point, with a unified interface and payment methods. To realize this vision, a wide range of diverse actors of the transportation ecosystem must communicate, interact, and cooperate with one another. Divergence of transportation standards and heterogeneity of data representations, formats and models are the main obstacles towards making such an interoperable system a reality. Hence, solutions are needed that bridge this fragmentation, hide the peculiarities of different standards and allow for the communication and exchange of data among heterogeneous, non-integrated systems. In line with this objective, The SPRINT (Semantics for PerfoRmant and scalable INteroperability of multimodal Transport) project aims at developing tools and technologies that facilitate interoperability in the transport domain. The core idea underlying the project is to go beyond pure \syntactic" interoperability|where interested parties are forced to adopt a unified set of formats for data exchange|and instead leverage \semantic" interoperability, which enables different systems to communicate with each other through their native standards, by mapping their concepts to a common ontology, which provides an unambiguous and homogeneous view of data.