A hybrid approach combining real-time and archived data for mobility analysis

Abstract

Mobility analysis is involved in many areas such as urban planning, traffic monitoring, climatology, study of social and animal phenomena to mention a few examples. The emergence and proliferation of mobile and sensor-based systems generate a significant increase of spatial and temporal data in terms of volume and frequency of update. In particular, the storage, management and analysis of the large data sets generated become a non straightforward task. Current works related to the manipulation of mobility data have been directed towards either mining archived historical data or continuous processing of incoming data streams. Our research introduces a hybrid approach whose objective is to provide a combined processing of real-time data streams and archived data. The principles of our approach is to promote the distributed and parallelized processing of mobility data. The whole framework is currently applied to the real-time monitoring of maritime traffic.