J Intell Inf Syst (2012) 38:343–391 DOI 10.1007/s10844-011-0159-2 Visually exploring movement data via similarity-based analysis Nikos Pelekis · Gennady Andrienko · Natalia Andrienko · Ioannis Kopanakis · Gerasimos Marketos · Yannis Theodoridis Received: 20 May 2010 / Revised: 30 March 2011 / Accepted: 30 March 2011 / Published online: 19 April 2011 © Springer Science+Business Media, LLC 2011 Abstract Data analysis and knowledge discovery over moving object databases dis- covers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the N. Pelekis (B ) Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece e-mail: npelekis@unipi.gr G. Andrienko · N. Andrienko Fraunhofer Institute Intelligent Analysis and Information Systems, Sankt Augustin, Germany G. Andrienko e-mail: gennady.andrienko@iais.fraunhofer.de N. Andrienko e-mail: natalia.andrienko@iais.fraunhofer.de I. Kopanakis Tech. Educational Institute of Crete, Ierapetra Crete, Greece e-mail: i.kopanakis@emark.teicrete.gr G. Marketos · Y. Theodoridis Department of Informatics, University of Piraeus, Piraeus, Greece G. Marketos e-mail: marketos@unipi.gr Y. Theodoridis e-mail: ytheod@unipi.gr