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Yannis Theodoridis
Researcher at University of Piraeus
Publications - 236
Citations - 10009
Yannis Theodoridis is an academic researcher from University of Piraeus. The author has contributed to research in topics: Spatial database & Spatial query. The author has an hindex of 47, co-authored 223 publications receiving 9426 citations. Previous affiliations of Yannis Theodoridis include National and Kapodistrian University of Athens & Research Academic Computer Technology Institute.
Papers
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Journal ArticleDOI
State-of-the-art in privacy preserving data mining
Vassilios S. Verykios,Elisa Bertino,Igor Nai Fovino,Loredana Parasiliti Provenza,Yucel Saygin,Yannis Theodoridis +5 more
TL;DR: An overview of the new and rapidly emerging research area of privacy preserving data mining is provided, and a classification hierarchy that sets the basis for analyzing the work which has been performed in this context is proposed.
Journal ArticleDOI
Semantic trajectories modeling and analysis
Christine Parent,Stefano Spaccapietra,Chiara Renso,Gennady Andrienko,Natalia Andrienko,Vania Bogorny,Maria Luisa Damiani,Aris Gkoulalas-Divanis,José Antônio Fernandes de Macêdo,Nikos Pelekis,Yannis Theodoridis,Zhixian Yan +11 more
TL;DR: A survey of the approaches and techniques for constructing trajectories from movement tracks, enriching trajectories with semantic information to enable the desired interpretations of movements, and using data mining to analyze semantic trajectories to extract knowledge about their characteristics.
Proceedings Article
Novel Approaches to the Indexing of Moving Object Trajectories
TL;DR: This work introduces two access methods for accessing trajectories of moving point objects, namely the Spatio-Temporal R-tree (STR-tree) and the Trajectory-Bundle tree (TB-tree), and presents guidelines for a successful choice among them.
Book ChapterDOI
On the Generation of Spatiotemporal Datasets
TL;DR: An algorithm, called "Generate_Spatio_Temporal_Data" (GSTD), which generates sets of moving point or rectangular data that follow an extended set of distributions is proposed, and some actual generated datasets are presented.