Dealing with trajectory streams by clustering and mathematical transforms

Nowadays, almost all kind of electronic devices leave traces of their movements (e.g. smartphone, GPS devices and so on). Thus, the huge number of this "tiny" data sources leads to the generation of massive data streams of geo-referenced data. As a matter of fact, the effective analysis of such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data streams management poses new challenges both for their proper definition and acquisition, thus making the overall process harder than for classical point data. In particular, we are interested in solving the problem of effective trajectory data streams clustering, that revealed really intriguing as we deal with sequential data that have to be properly managed due to their ordering. We propose a framework that allow data pre-elaboration in order to make the mining step more effective. As for every data mining tool, the experimental evaluation is crucial, thus we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed approach. © 2013 Springer Science+Business Media New York.