Ontology-driven data integration for railway asset monitoring applications

Abstract

As the extent to which information systems are used across rail and transportation networks continues to grow, huge potential for data driven decision support and analysis is emerging. Interoperability between systems in these industries is currently poor, and opportunities for such analysis are often missed through unavailability of data. Semantic data modeling provides a mechanism for facilitating greater interoperability between systems, and allows easier integration of data from heterogeneous sources. This paper describes the current state of the art in rail data modeling, and introduces a asset monitoring system based on contemporary ontology and linked data (semantic data modeling) technologies. The design and implementation of Asset Monitoring As A Service (AMaaS) is shown, and overviews of key design patterns used to ensure extensibility and interoperability given. Finally, the potential for re-use of the system is discussed, along with known limitations and known technology advances. An outline of further work is provided, including in designing methodologies to foster uptake semantic data models across the industry.