[av_heading heading=’Mobility Data Exploration’ tag=’h2′ color=” style=” padding=’10’]
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Mobility data is ubiquitous, particularly due to the automated collection of time-stamped location information from GPS-equipped devices, from everyday smartphones to dedicated software and hardware in charge of monitoring move-ment in land, sea, and air. Such wealth of data, referenced both in space and time, enables novel classes of applications and services of high societal and economic impact, provided that the discovery of consumable and concise knowledge out of these raw data collections is made possible. Mobility data science is about understanding and exploiting on mobility data: collecting and cleansing data, storage in Moving Object Database (MOD) engines, indexing, processing, analyzing, and mining mobility data. Emerging issues, such as semantic- and privacy-aware querying and mining, as well as distributed processing of mobility data compose a central piece in the BIG data puzzle.
We have been working for almost two decades in the field of mobility data management and exploration, developing models, techniques, and tools that cover the complete flow of information, from data storage to knowledge extraction. Research highlights include:
- Hermes MOD engine
- Hermessem semantically-aware trajectory management and mining tool
- Hermoupolis trajectory data generator
- ReTraTree indexing technique for the management of trajectories in the big data era
As principal investigators of several EU projects, such as GeoPKDD, MODAP, MOVE, DATASIM, SEEK, we have the required perception and capacity to further guide research in the field.
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[av_iconlist_item title=’Selected Publications’ link=” linktarget=” icon=’ue84d’ font=’entypo-fontello’]
| N. Pelekis, S. Sideridis, Y. Theodoridis: “Hermessem: a Semantic-aware Framework for the Management and Analysis of our LifeSteps”, IEEE/ACM International Conference on Data Science and Advanced Analytics (IEEE DSAA’2015), Paris, 2015, IEEE CS Press. |
| N. Pelekis, E. Frentzos, N. Giatrakos, Y. Theodoridis: “HERMES: A Trajectory DB Engine for Mobility-Centric Applications”, Int’l Journal of Knowledge-based Organizations (IJKBO), 5(3), 2015. IGI Global. |
| N. Pelekis, Y. Theodoridis: Mobility Data Management and Exploration. Springer, New York, 2014, in press. ISBN 978-1-4939-0391-7. |
| B. Krogh, N. Pelekis, Y. Theodoridis, K. Torp: “Path-based Queries on Trajectory Data”, Proceedings of the 22nd Int’l Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL’14, Dallas/Fort Worth, TX, USA, November 2014. ACM Press. |
| C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. L. Damiani, A. Gkoulalas-Divanis, J. Macedo, N. Pelekis, Y. Theodoridis, Z. Yan: “Semantic Trajectories Modeling and Analysis”, ACM Computing Surveys, 45(4), December 2013. ACM Press. |
| N. Pelekis, Y. Theodoridis, D. Janssens: “On the management and analysis of our LifeSteps”, SIGKDD Explorations, 15(1):23-32, June 2013. ACM. |
| G. Kellaris, N. Pelekis, Y. Theodoridis: “Map-Matched Trajectory Compression”, Journal of Systems and Software, 86(6):1566-1579, June 2013. Elsevier. |
| N. Pelekis, C. Ntrigkogias, P. Tampakis, S. Sideridis, Y. Theodoridis: “Hermoupolis: A Trajectory Generator for Simulating Generalized Mobility Patterns”, demo paper, Proceedings of the European Conference on Machine Learning / Principles and Practice of Knowledge Discovery in Databases, ECML/PKDD’13, Prague, Czech Republic, September 2013. Springer. |
| C. Panagiotakis, N. Pelekis, I. Kopanakis, E. Ramasso, Y. Theodoridis: “Segmentation and Sampling of Moving Object Trajectories based on Representativeness”, IEEE Transactions on Knowledge and Data Engineering, 24(7):1328-1343, July 2012. IEEE CS Press. |
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