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Smart City Science Group
Fakultät für Informatik
Grafik: Zeichnungen eines Kopfes, eines Rades und einer Pipette in Regenbogenfarben © Andrew Ostrovsky

Ausgewählte Publikationen

2022

  • A. Becker and T. Liebig, “Certified Data Removal in Sum-Product Networks,” in IEEE International Conference on Knowledge Graph (ICKG), 2022. (ICKG 2022)
  • A. Roth and T. Liebig, “Transforming PageRank into an Infinite-Depth Graph Neural Network,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2022. (ECML PKDD 2022, Best Paper Award)
  • D. Boiar, N. Killich, L. Schulte, V. H. Moreno, J. Deuse, and T. Liebig, “Forecasting Algae Growth in Photo-Bioreactors using Attention LSTMs,” in Proceedings of the Workshop on Artificial Intelligence for Engineering Applications 2022, 2022.
  • T. Sachweh, D. Boiar, and T. Liebig, “Distributed LSTM-Learning from Differentially Private Label Proportions,” in 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1071-1078, 2022.
  • A. Roth and T. Liebig, “Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks,” in 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 740-747, 2022.
  • X. Shao, A. Molina, A. Vergari, K. Stelzner, R. Peharz, T. Liebig, and K. Kersting, “Conditional sum-product networks: Modular probabilistic circuits via gate functions,” International Journal of Approximate Reasoning, vol. 140, pp. 298-313, 2022. (IJAR)

2021

  • M. Klumpp, M. Hintze, M. Immonen, F. Ródenas-Rigla, F. Pilati, F. Aparicio-Martínez, D. Çelebi, T. Liebig, M. Jirstrand, O. Urbann, and others, “Artificial intelligence for hospital health care: Application cases and answers to challenges in european hospitals,” in Healthcare, vol. 9, iss. 8, p. 961, 2021. (Healthcare)
  • P. Haritz, L. Pfahler, T. Liebig, and H. Kotthaus, “Self-Supervised Source Code Annotation from Related Research Papers,” in 2021 International Conference on Data Mining Workshops (ICDMW), pp. 1083-1084, 2021.
  • T. Sachweh, D. Boiar, and T. Liebig, “Differentially Private Learning from Label Proportions,” in International Workshops of ECML PKDD 2021, pp. 119-127, 2022.
  • A. Roth, K. Wüstefeld, and F. Weichert. “A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation,” in Journal of Imaging, vol. 7, iss. 10, 2021. (Journal of Imaging)

2020

  • C. Sanders and T. Liebig, “Knowledge Discovery on Blockchains: Challenges and Opportunities,” in Proceedings of the ECML Workshop on Parallel, Distributed, and Federated Learning, 2020.
  • X. Shao, A. Molina, A. Vergari, K. Stelzner, R. Perharz, T. Liebig, and K. Kersting, “Conditional Sum-Product Networks: Composing Neural Networks into Probabilistic Tractable Models,” in Proceedings of the 10th International Conference on Probabilistic Graphical Models, 2020. (ICPGM 2020)
  • F. Ziegler, M. Freund, A. Rydzek, and T. Liebig, “Towards Truck Parking Lot Occupancy Estimation,” arXiv preprint arXiv:2002.00244, 2020.

2019

  • B. Sliwa, R. Falkenberg, T. Liebig, N. Piatkowski, and C. Wietfeld, “Boosting vehicle-to-cloud communication by machine learning-enabled context prediction,” in IEEE Transactions on Intelligent Transportation Systems, vol. 21, iss. 8, pp. 3497-3512, 2019. (T-ITS)
  • B. Sliwa, T. Liebig, T. Vranken, M. Schreckenberg, and C. Wietfeld, “System-of-systems modeling, analysis and optimization of hybrid vehicular traffic,” in 2019 Annual IEEE International Systems Conference (SysCon), 2019. (SysCon 2019)
  • S. Buschjäger, T. Liebig, and K. Morik, “Gaussian Model Trees for Traffic Imputation,” in 8th International Conference on Pattern Recognition Applications and Methods, 2019, pp. 243-254. (ICPRAM 2019)
  • B. Sliwa, R. Falkenberg, T. Liebig, N. Piatkowski, and C. Wietfeld, “Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction,” IEEE Transactions on Intelligent Transportation Systems, pp. 1-18, 2019. (T-ITS)

2018

  • M. Rieke, L. Bigagli, S. Herle, S. Jirka, A. Kotsev, T. Liebig, C. Malewski, T. Paschke, and C. Stasch, “Geospatial IoT — The Need for Event-Driven Architectures in Contemporary Spatial Data Infrastructures,” ISPRS International Journal of Geo-Information, vol. 7, iss. 10, 2018. (IJGI)
  • C. Römer, J. Hiry, C. Kittl, T. Liebig, and C. Rehtanz, “Charging control of electric vehicles using contextual bandits considering the electrical distribution grid,” 2018.
  • B. Sliwa, T. Liebig, R. Falkenberg, J. Pillmann, and C. Wietfeld, “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 (VTC Spring), pp. 1-6, 2018. (VTC Spring 2018)
  • B. Sliwa, R. Falkenberg, T. Liebig, J. Pillmann, and C. Wietfeld, “Machine Learning Based Context-predictive Car-to-cloud Communication Using Multi-layer Connectivity Maps for Upcoming 5G Networks,” in Proceedings of the 88th Vehicular Technology Conference (VTC-Fall), p. 1-7, 2018. (VTC Fall 2018)
  • D. Tomaras, V. Kalogeraki, T. Liebig, and D. Gunopulos, “Crowd-based ecofriendly trip planning,” in Proceedings of the 19th IEEE International Conference on Mobile Data Management, pp. 24-33, 2018. (MDM 2018)
  • B. Sliwa, T. Liebig, R. Falkenberg, J. Pillmann, and C. Wietfeld, “Resource-efficient Transmission of Vehicular Sensor Data Using Context-aware Communication,” in Proceedings of the 19th IEEE International Conference on Mobile Data Management, pp. 282-283, 2018. (MDM 2018)

2017

  • N. Shaik, T. Liebig, C. Kirsch, and H. Müller, “Dynamic Map Update of Non-static Facility Logistics Environment with a Multi-robot System,” in KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference on AI, Dortmund, Germany, September 25–29, 2017, pp. 249-261, 2017.
  • L. Heppe and T. Liebig, “Real-Time Public Transport Delay Prediction for Situation-Aware Routing,” in KI 2017: Advances in Artificial Intelligence: 40th Annual German Conference on AI, Dortmund, Germany, September 25–29, 2017, pp. 128-141, 2017.
  • T. Liebig, S. Peter, M. Grzenda, and K. Junosza-Szaniawski, “Dynamic Transfer Patterns for Fast Multi-modal Route Planning,” in Societal Geo-innovation: Selected papers of the 20th AGILE conference on Geographic Information Science, pp. 223-236, 2017.
  • T. Liebig and M. Sotzny, “On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning,” in 13th International Conference on Spatial Information Theory (COSIT 2017), vol. 86, p. 17:1–17:12, 2017. (COSIT 2017)
  • T. Liebig, N. Piatkowski, C. Bockermann, and K. Morik, “Dynamic Route Planning with Real-Time Traffic Predictions,” Information Systems, vol. 64, pp. 258-265, 2017. (IS)

2016

  • G. Souto and T. Liebig, “On Event Detection from Spatial Time series for Urban Traffic Applications,” in Solving Large Scale Learning Tasks: Challenges and Algorithms, vol. 9580, pp. 221-233, 2016.
  • N. Panagiotou, N. Zygouras, I. Katakis, D. Gunopulos, N. Zacheilas, I. Boutsis, V. Kalogeraki, S. Lynch, B. O’Brien, D. Kinane, J. Mareček, J. Y. Yu, R. Verago, E. Daly, N. Piatkowski, T. Liebig, C. Bockermann, K. Morik, F. Schnitzler, M. Weidlich, A. Gal, S. Mannor, H. Stange, W. Halft, and G. Andrienko, “INSIGHT: Dynamic Traffic Management Using Heterogeneous Urban Data,” in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, pp. 22-26, 2016. (ECML PKDD 2016)

2015

  • G. Souto and T. Liebig, “Analyzing the Correlation Among Traffic Loop Sensors to Detect Anomalies in Traffic Loop Data Streams,” in Proceedings of the 3rd Symposium on Knowledge Discovery and Machine Learning, pp. 82-90, 2015.
  • I. Katakis, F. Schnitzler, and T. Liebig, “2nd International Workshop on Mining Urban Data (Preface),” in Proceedings of the 2nd International Workshop on Mining Urban Data (MUD2), vol. 1392, pp. 7-9, 2015.
  • T. Liebig, “Privacy Preserving Centralized Counting of Moving Objects,” in AGILE: International Conference on Geographic Information Science, pp. 91-103, 2015. (AGILE 2015)
  • T. Liebig, “AI-based analysis methods in spatio-temporal data mining,” in AI: Philosophy, Geoinformatics & Law, pp. 135-152, 2015.
  • M. Stolpe, T. Liebig, and K. Morik, “Communication-efficient learning of traffic flow in a network of wireless presence sensors,” in Proceedings of the Workshop on Parallel and Distributed Computing for Knowledge Discovery in Data Bases (PDCKDD 2015), 2015.
  • T. Liebig, M. Stolpe, and K. Morik, “Distributed Traffic Flow Prediction with Label Proportions: From in-Network towards High Performance Computation with MPI,” in Proceedings of the 2nd International Workshop on Mining Urban Data (MUD2), vol. 1392, pp. 36-43, 2015.
  • T. Liebig, S. Storandt, P. Sanders, W. Othman, and S. Funke, “Report from Dagstuhl: SocioPaths – Multimodal Door-to-Door Route Planning via Social Paths,” in Proceedings of the 2nd International Workshop on Mining Urban Data (MUD2), vol. 1392, pp. 90-94, 2015.

2014

  • T. Liebig, N. Piatkowski, C. Bockermann, and K. Morik, “Predictive Trip Planning – Smart Routing in Smart Cities,” in Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014, 2014, pp. 331-338.
  • D. Kinane, F. Schnitzler, S. Mannor, T. Liebig, K. Morik, J. Marecek, B. Gorman, N. Zygouras, Y. Katakis, V. Kalogeraki, and D. Gunopulos, “Intelligent Synthesis and Real-time Response using Massive Streaming of Heterogeneous Data (INSIGHT) and its anticipated effect on Intelligent Transport Systems (ITS) in Dublin City, Ireland,” in Proceedings of the 10th ITS European Congress, Helsinki, pp. 1-6, 2014.
  • T. Liebig, “Stream Processing and Crowdsourcing for Urban Traffic Management,” in Dagstuhl Seminar 13512: Social Issues in Computational Transportation Science, pp. 104-105, 2014.
  • T. Liebig, “Privacy Preserving Aggregation of Distributed Mobility Data Streams,” in Proceedings of the 11th Symposium on Location-Based Services, 2014, pp. 86-99.
  • T. Liebig, “Speed-Up heuristics for the traffic flow estimation with Gaussian Process Regression,” in Proceedings of the 11th Symposium on Location-Based Services, pp. 136-138, 2014.
  • T. Liebig, N. Piatkowski, C. Bockermann, and K. Morik, “Route Planning with Real-Time Traffic Predictions,” in Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, pp. 83-94, 2014.
  • F. Schnitzler, T. Liebig, S. Mannor, G. Souto, S. Bothe, and H. Stange, “Heterogeneous Stream Processing for Disaster Detection and Alarming,” in IEEE International Conference on Big Data, pp. 914-923, 2014. (IEEE BigData 2023)
  • F. Schnitzler, A. Artikis, M. Weidlich, I. Boutsis, T. Liebig, N. Piatkowski, C. Bockermann, K. Morik, V. Kalogeraki, J. Marecek, A. Gal, S. Mannor, D. Kinane, and D. Gunopulos, “Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights,” in European Conference on Machine Learning and Knowledge Discovery in Databases, vol. 8726, pp. 520-523, 2014. (ECML PKDD 2014)
  • F. Schnitzler, T. Liebig, S. Mannor, and K. Morik, “Combining a Gauss-Markov model and Gaussian process for traffic prediction in Dublin city center,” in Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference (EDBT/ICDT 2014), pp. 373-374, 2014.
  • A. Artikis, M. Weidlich, F. Schnitzler, I. Boutsis, T. Liebig, N. Piatkowski, C. Bockermann, K. Morik, V. Kalogeraki, J. Marecek, A. Gal, S. Mannor, D. Gunopulos, and D. Kinane, “Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management,” in Proc. 17th International Conference on Extending Database Technology (EDBT), pp. 712-723, 2014. (EDBT 2014)
  • T. Liebig, G. Andrienko, and N. Andrienko, “Methods for Analysis of Spatio-Temporal Bluetooth Tracking Data,” Journal of Urban Technology, vol. 21, iss. 2, pp. 27-37, 2014. (Journal of Urban Technology)

2013

  • T. Liebig, Z. Xu, and M. May, “Incorporating Mobility Patterns in Pedestrian Quantity Estimation and Sensor Placement,” in International Workshop on Citizen in Sensor Networks, pp. 67-80, 2013.
  • R. Rösler and T. Liebig, “Using Data from Location Based Social Networks for Urban Activity Clustering,” in Geographic Information Science at the Heart of Europe, pp. 55-72, 2013.
  • G. Andrienko, A. Gkoulalas-Divanis, M. Gruteser, C. Kopp, T. Liebig, and K. Rechert, “Report from Dagstuhl: the liberation of mobile location data and its implications for privacy research,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 17, iss. 2, pp. 7-18, 2013. (MC2R)

2012

  • T. Liebig and A. U. Kemloh Wagoum, “Modelling Microscopic Pedestrian Mobility using Bluetooth,” in Proc. of the Fourth International Conference on Agents and Artificial Intelligience, pp. 270-275, 2012. (ICAART 2012)
  • N. Andrienko, G. Andrienko, H. Stange, T. Liebig, and D. Hecker, “Visual Analytics for Understanding Spatial Situations from Episodic Movement Data,” KI-Künstliche Intelligenz, pp. 1-11, 2012.
  • T. Ellersiek, T. Liebig, D. Hecker, and C. Körner, “Analyse von raumzeitlichen Bewegungsmustern auf Basis von Bluetooth-Sensoren,” in Angewandte Geoinformatik 2012, pp. 260-269, 2012.
  • T. Liebig and Z. Xu, “Pedestrian monitoring system for indoor billboard evaluation,” Journal of Applied Operational Research, vol. 4, pp. 28-36, 2012. (JAOR)
  • P. Utsch and T. Liebig, “Monitoring Microscopic Pedestrian Mobility Using Bluetooth,” in 2012 Eighth International Conference On Intelligent Environments, pp. 173-177, 2012. (IE 2012)
  • T. Liebig, Z. Xu, M. May, and S. Wrobel, “Pedestrian quantity estimation with trajectory patterns,” in Proceedings of the 2012th European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 629-643, 2012. (ECML PKDD 2012)
  • G. Andrienko, A. Gkoulalas-Divanis, M. Gruteser, C. Körner, T. Liebig, K. Rechert, and M. Marhöfer, “4 Working Groups 4.1 Working Group: Cellular Data,” Mobility Data Mining and Privacy, 2012.

2011

  • T. Liebig, “Trajectory Regression Model for Indoor Pedestrian Flow Analysis on Billboard Evaluation,” in International Conference on Applied Operational Research (ICAOR), pp. 289-300, 2011. (ICAOR 2011)
  • H. Stange, T. Liebig, D. Hecker, G. Andrienko, and N. Andrienko, “Analytical workflow of monitoring human mobility in big event settings using bluetooth,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, pp. 51-58, 2011.

2010

  • T. Liebig, H. Stange, D. Hecker, M. May, C. Körner, and U. Hofmann, “A general pedestrian movement model for the evaluation of mixed indoor-outdoor poster campaigns,” Proc. of the Third International Workshop on Pervasive Advertising and Shopping, 2010.

2009

  • T. Liebig, C. Körner, and M. May, “Fast visual trajectory analysis using spatial bayesian networks,” in 2009 IEEE International Conference on Data Mining Workshops, pp. 668-673, 2009.

2008

  • T. Liebig, C. Körner, and M. May, “Scalable sparse bayesian network learning for spatial applications,” in 2008 IEEE International Conference on Data Mining Workshops, pp. 420-425, 2008.
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