Hermoupolis

Abstract

The domain of trajectory data management and mining undoubtedly contributes with interesting research problems and corresponding effective solutions to what is called data science. An interesting trend that poses new challenges in the field and has emerged especially due to the advance of location-based social networks, is that involved data cannot be considered purely spatiotemporal; trajectories of moving objects also contain additional semantic information that deserves to be further explored. On the other hand, the recently available real trajectory datasets are neither adequate nor appropriate for a wide empirical evaluation of related research proposals. As in other domains, a practical approach to overcome this limitation is developing efficient and functional synthetic trajectory generators. In this line of research, we present Hermoupolis, a pattern- and semantic-aware synthetic trajectory generator, which is able to produce realistic semantic trajectory datasets (along with their synchronized raw spatiotemporal counterparts), conforming to mobility profiles given as input by users.