VaVeL: Variety, Veracity, VaLue - Handling the Multiplicity of Urban Sensors




Urban environments are awash with data from fixed and mobile sensors and monitoring infrastructures from public, private, or industry sources. Making such data useful would enable developing novel big data applications to benefit the citizens of Europe in areas such as transportation, infrastructures, and crime prevention. Urban data is heterogeneous, noisy, and unlabeled, which severely reduces its usability. Succinctly stated, urban data are difficult to understand. The goal of the VaVeL project is to radically advance our ability to use urban data in applications that can identify and address citizen needs and improve urban life. Our motivation comes from problems in urban transportation. This project will develop a general purpose framework for managing and mining multiple heterogeneous urban data streams for cities become more efficient, productive and resilient. The framework will be able to solve major issues that arise with urban transportation related data and are currently not dealt by existing stream management technologies. The project brings together two European cities that provide diverse large scale data of cross-country origin and real application needs, three major European companies in this space, and a strong group of researchers that have uniquely strong expertise in analyzing real-life urban data. VaVeL aims at making fundamental advances in addressing the most critical inefficiencies of current (big) data management and stream frameworks to cope with emerging urban sensor data thus making European urban data more accessible and easy to use and enhancing European industries that use big data management and analytics. The consortium develops end-user driven concrete scenaria that are addressing real, important problems with the potential of enormous impact, and a large spectrum of technology requirements, thus enabling the realization of the fundamental capabilities required and the realistic evaluation of the success of our methods.



Staff Members:

Morik, Katharina
Pölitz, Christian




Buschjaeger/etal/2019a Buschjäger, Sebastian and Liebig, Thomas and Morik, Katharina. Gaussian Model Trees for Traffic Imputation. In Proceedings of the International Conference on Pattern Recognition Applications and Methods (ICPRAM), pages 243 - 254, SciTePress, 2019. Arrow Symbol
Sliwa/etal/2018a Sliwa, Benjamin and Liebig, Thomas and Falkenberg, Robert and Pillmann, Johannes and Wietfeld, Christian. Efficient machine-type communication using multi-metric context-awareness for cars used as mobile sensors in upcoming 5G networks. In Proceedings of the 87th Vehicular Technology Conference: VTC2018-Spring, IEEE, 2018.
Tomaras/etal/2018a Dimitrios Tomaras and Vana Kalogeraki and Thomas Liebig and Dimitrios Gunopulos. Crowd-based ecofriendly trip planning. In Proceedings of the 19th IEEE International Conference on Mobile Data Management, Aalborg, pages (accepted), IEEE Press, 2018.
Heppe/2017a Heppe, Lukas and Liebig, Thomas. Real-Time Public Transport Delay Prediction for Situation-Aware Routing. In Kern-Isberner, Gabriele and Fürnkranz, Johannes and Thimm, Matthias (editors), KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference on AI, Dortmund, Germany, September 25--29, 2017, Proceedings, pages 128--141, Cham, Springer, 2017. Arrow Symbol
Liebig/2017a Liebig, Thomas. Smart navigation - chances, risk and challenges. In M. Jankowska and M. Pawelczyk and S. Augustyn and M. Kulawiak (editors), Navigation and Earth Observation - Law & Technology, pages (accepted), Warsaw, IUS PUBLICUM, 2017.
Liebig/2017b Liebig, Thomas. Report on Data Privacy. No. H2020-688380 D4.1, VAVEL Consortium, Dortmund, Germany, 2017.
Liebig/etal/2017b Liebig, Thomas and Piatkowski, Nico and Bockermann, Christian and Morik, Katharina. Dynamic Route Planning with Real-Time Traffic Predictions. In Information Systems, Vol. 64, pages 258--265, Elsevier, 2017. Arrow Symbol
Liebig/Sotzny/2017a Liebig, Thomas and Sotzny, Maurice. On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning. In Clementini, Eliseo and Donnelly, Maureen and Yuan, May and Kray, Christian and Fogliaroni, Paolo and Ballatore, Andrea (editors), 13th International Conference on Spatial Information Theory (COSIT 2017), Vol. 86, pages 17:1--17:12, Dagstuhl, Germany, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2017. Arrow Symbol
Souto/Liebig/2016a Gustavo Souto and Thomas Liebig. On Event Detection from Spatial Time Series for Urban TrafficApplications. In Stefan Michaelis and Nico Piatkowski and Marco Stolpe (editors), Solving Large Scale Learning Tasks: Challenges and Algorithms, Vol. 9580, pages 221--233, Springer, 2016. Arrow Symbol
Liebig/2015b Liebig, Thomas. Analysis Methods and Privacy Aspects in Spatio-Temporal Data Mining. In Marlena Jankowska and Miroslaw Pawelczyk and Sylvie Allouche and Marcin Kulawiak (editors), AI: Philosophy, Geoinformatics & Law, pages (to appear), Warsaw, IUS PUBLICUM, 2015.