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:

Liebig, Thomas
Morik, Katharina
Pölitz, Christian




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.
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/2017a Liebig, Thomas and Peter, Sebastian and Grzenda, Maciej and Junosza-Szaniawski, Konstanty. Dynamic Transfer Patterns for Fast Multi-modal Route Planning. In Bregt, Arnold and Sarjakoski, Tapani and van Lammeren, Ron and Rip, Frans (editors), Societal Geo-innovation: Selected papers of the 20th AGILE conference on Geographic Information Science, pages 223--236, Cham, Springer, 2017.
Liebig/etal/2017b Thomas Liebig and Nico Piatkowski and Christian Bockermann and Katharina Morik. Dynamic Route Planning with Real-Time Traffic Predictions. In Information Systems, Vol. 64, pages 258--265, Elsevier, 2017.
Liebig/Sotzny/2017a Thomas Liebig and Maurice Sotzny. On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning. In Eliseo Clementini, Maureen Donnelly, May Yuan, Christian Kray, Paolo Fogliaroni, and Andrea Ballatore (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.
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.
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.