In later years, the increase in computation power and data storage has opened new perspectives to data analysis. The possibility to analyse big data brings new insights into obscure and useful correlations in data providing undiscovered knowledge. Applying big data analytics to the transport data has brought better understanding to the transports network revealing unexpected choking points in cities. This technology is still largely inaccessible to small companies due to their limited computational resources and complex for large ones due to the time needed to develop a big data analytical system Using the high scalability of Cloud and the use of specialized services in a services oriented architecture, new perspective are to developing efficient and scalable big data infrastructure adapted to transport systems. This paper presents a big data infrastructure using service oriented architecture.

We thank the Région Rhône-Alpes who finances the thesis work of Gavin Kemp by means of the ARC 7 programme (http://www.arc7-territoiresmobilites. rhonealpes.fr/), as well as the competitiveness cluster LUTB Transport & Mobility Systems.

ITS,Big Data,Cloud Services,NoSQL

• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology