Marios Vodas

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Existing approaches for privacy-aware mobility data sharing aim at publishing an anonymized version of the mobility dataset, operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violations. In this paper, we assume that the majority of the information that exists in the mobility(More)
We present a system that combines intelligent online tracking with complex event recognition against streaming positions relayed from numerous vessels. Given the vital importance of maritime safety to the environment, the economy, and in national security, our system leverages the real-time acquisition of vessel activity with geographical and other static(More)
Mobility data sources feed larger and larger trajectory databases nowadays. Due to the need of extracting useful knowledge patterns that improve services based on users' and customers' behavior, querying and mining such databases has gained significant attention in recent years. However, publishing mobility data may lead to severe privacy violations. In(More)
Even though ship accidents at sea has many economic and environmental implications on Greece, ships formulate routes according to their best judgment. In this study we take a dataset spanning in 2.5 years from the AIS network, which is transmitting in public a ship's identity and location, and we load it in a trajectory database supported by the Hermes MOD(More)
Maritime monitoring is a typical Big Data problem where hundreds of thousands of vessels across the globe transmit messages about their location, speed and other information. We have developed a system for online vessel tracking that performs, as a first step, a high-rate but accurate trajectory compression. Subsequently, the compressed trajectories are(More)
We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. It employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks(More)
1 Mobility data sources feed larger and larger trajectory databases nowadays. Due to the need of extracting useful knowledge patterns that improve services based on users' and customers' behavior, querying and mining such databases has gained significant attention in recent years. Existing approaches for privacy-aware mobility data sharing aim at publishing(More)
ions. In the first abstraction layer, raw trajectory data are preprocessed and then partitioned into subsequences based on various policies. In the third layer, meaningful episodes are defined with the use of various criteria such as velocity, density, orientation which are finally annotated with additional contextual data from application and geographical(More)
Acknowledgements I would like to thank Yannis Theodoridis and Nikos Pelekis for their insight-ful supervision during the writing of my thesis. I would also like to thank my colleagues Despina Kopanaki, Panagiotis Tampakis, Nikos Giatrakos, Stelios Sideridis, Giannis Kostis, and Michalis Basios for our cooperation in the lab. Last but not least, I would like(More)
Cluster analysis over Moving Object Databases (MODs) is a challenging research topic that has attracted the attention of the mobility data mining community. In this paper, we study the temporal-constrained sub-trajectory cluster analysis problem, where the aim is to discover clusters of sub-trajectories given an ad-hoc, user-specified temporal constraint(More)