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Publications

■ IoT Data Analytics – A key enabler for the Growth of Smart Cities
Manuel Görtz, Martin Strohbach, Markus Schlattmann, “IoT Data Analytics – A key enabler for the Growth of Smart Cities", SocietyByte – Wissenschaftsmagazin des BFH-Zentrums Digital Society, 2017
■ Smart navigation – chances, risk and challenges
T. Liebig, “Smart navigation – chances, risk and challenges,” in Navigation and Earth Observation – Law & Technology, M. Jankowska, M. Pawelczyk, S. Augustyn, and M. Kulawiak, Eds., Warsaw: IUS PUBLICUM, 2017, p. (accepted).

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■ On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning
T. Liebig and M. Sotzny, “On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning,” in Proceedings of the 13th International Conference on Spatial Information Theory COSIT, E. Clementini, M. Donnelly, M. Yuan, C. Kray, P. Fogliaroni, and A. Ballatore, Eds., L’Aquila, Italy: , 2017 (accepted).

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■ Dynamic Transfer Patterns for Fast Multi-modal Route Planning
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, A. Bregt, T. Sarjakoski, R. van Lammeren, and F. Rip, Eds., Cham: Springer International Publishing, 2017, pp. 223-236.

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■ Travel Time Prediction for Trams in Warsaw
Zychowski, A., Junosza-Szaniawski, K., & Kosicki, A. (2017, May). Travel Time Prediction for Trams in Warsaw. In International Conference on Computer Recognition Systems, CORES 2017(pp. 53-62). Springer, Cham.

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■ Clustering of Mobile Subscriber's Location Statistics for Travel Demand Zones Diversity
Luckner M., Rosłan A., Krzemińska I., Legierski J., Kunicki R. (2017) Clustering of Mobile Subscriber’s Location Statistics for Travel Demand Zones Diversity. In: Saeed K., Homenda W., Chaki R. (eds) Computer Information Systems and Industrial Management. CISIM 2017. Lecture Notes in Computer Science, vol 10244. Springer, Cham.
To optimise a public transport infrastructure it is necessary to gather information on citizen demand on that subject. However, the data gathering is a laborious and costly task. Fig. 1 shows how Base Transceiver Stations (BTS) register a daily characteristic of mobile events occurrences. In our work, we proposed how to use the daily statistics to find similar travel demand zones from the Warsaw public transport demand model. Fig. 2 presents obtained results. On the map, one can recognise separate areas that contain shopping centres or sleeping quarters. The created methodology can be used to recognise characteristical directions of mass movement or to detect anomalies in the daily routine.
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■ Public Transport Stops State Detection and Propagation - Warsaw Use Case
Luckner M., Kobojek P. and Zawistowski P. (2017). Public Transport Stops State Detection and Propagation - Warsaw Use Case.In Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-241-7, pages 235-241. DOI: 10.5220/0006305102350241
Publication of information on public transport in a form acceptable to third-party developers can improve a quality of services offered to the citizens. Usually, published data are limited to localisations of the stops and the schedules. However, a public transport model based on these data is incomplete without information about a current state of the stops. We present a system that observes public sources of information on public transport such as Twitter feeds and official web pages hosted by the City of Warsaw. Fig. 1 shows how the incoming messages are parsed to extract information on events that concern public transport lines and stops. Extracted information allows us to detect a current state of the stops and to create linguistically independent and spatial oriented information in Geography Markup Language format that can be published using a web service. Fig. 2 shows how the text message was automatically translated into a linguistically independent graphical presentation.
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■ State Transition Graphs for Semantic Analysis of Movement Behaviours
Andrienko, N., & Andrienko, G. (2017). State Transition Graphs for Semantic Analysis of Movement Behaviours. Information Visualization.
This paper presents a state transition graphs approach to analysis of movement data. Such a representation supports the exploration and analysis of the semantic aspect (i.e. the meaning or purposes) of movement.
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■ Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions
Gennady Andrienko, Natalia Andrienko, Wei Chen, Ross Maciejewski, and Ye Zhao, "Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions", IEEE Transactions on Intelligent Transportation Systems, 2017, vol. 18(8), pp.2232-2249
Paper [AAC+17] surveys the state of the art in visual analytics methods developed for mobility analysis and transportation applications and outlines directions for further research and applications. This paper served a general framework for visual analytics research and development in VaVeL.
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■ First Story Detection using Entities and Relations
Nikolaos Panagiotou, Cem Akkaya, Kostas Tsioutsiouliklis, Vana Kalogeraki, Dimitrios Gunopulos, First Story Detection using Entities and Relations. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3237–3244, Osaka, Japan, December 11-17 2016.
In this work we describe a novel framework that is able to identify a document that refers to a new event given a document stream. Due to the fact that in both the use cases, access to a stream of documents is available, this work could be effectively applied in order to detect articles or social content that describes a new event.
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■ An Active Learning Framework Incorporating User Input For Mining Urban Data
An Active Learning Framework Incorporating User Input For Mining Urban Data, Nikolaos Zygouras, Nikolaos Panagiotou, Nikos Zacheilas, Ioannis Boutsis, Vana Kalogeraki, Dimitrios Gunopulos , UrbComp 2016, San Francisco, California, USA, August 2016
In this work we describe a novel active learning framework for training a supervised model, for traffic event detection, considering both time and budget constraints. The proposed method, carefully selects informative instances to be annotated and addresses this way the label scarcity problem that is present on both of the VaVeL project use cases.
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■ Lightweight Monitoring of Distributed Streams
Arnon Lazerson, Daniel Keren, and Assaf Schuster. Lightweight Monitoring of Distributed Streams. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 1685-1694

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■ Gaining Knowledge from Georeferenced Social Media Data with Visual Analytics
Andrienko, G and Andrienko, N. 2016. Gaining Knowledge from Georeferenced Social Media Data with Visual Analytics. In: Capineri, C, Haklay, M, Huang, H, Antoniou, V, Kettunen, J, Ostermann, F and Purves, R. (eds.) European Handbook of Crowdsourced Geographic Information, pp. 157–167. London: Ubiquity Press.

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■ Exploration and Refinement of Regression Tree Models with Interactive Maps and Spatial Data Transformations
Gennady Andrienko, Natalia Andrienko, Alexander Ryumkin, Valery Ryumkin, Gennady Kravchenko, Evegeny Tyabaev, Dmitry Khloptsov, Svetlana Trofimova, "Exploration and Refinement of Regression Tree Models with Interactive Maps and Spatial Data Transformations", International Journal of Cartography, 2016, vol. 2(1), pp.59-76

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■ Revealing Patterns and Trends of Mass Mobility through Spatial and Temporal Abstraction of Origin-Destination Movement Data
Gennady Andrienko, Natalia Andrienko, Georg Fuchs, and Jo Wood Revealing Patterns and Trends of Mass Mobility through Spatial and Temporal Abstraction of Origin-Destination Movement Data IEEE Transactions on Visualization and Computer Graphics, 2017, vol. 23(9), pp.2120-2136
This paper addresses an important problem of analysing origin-destination (OD) movement data. The paper presents an approach facilitating exploration of long-term flow data by means of spatial and temporal abstraction. It involves a special way of data aggregation, which allows representing spatial situations by diagram maps instead of flow maps, thus reducing the intersections and occlusions pertaining to flow maps.
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■ Dynamic Route Planning with Real-Time Traffic Predictions
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.

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■ AI-based analysis methods in spatio-temporal data mining
T. Liebig, “AI-based analysis methods in spatio-temporal data mining,” in AI: Philosophy, Geoinformatics & Law, M. Jankowska, M. Pawelczyk, and M. Kulawiak, Eds., Warsaw: IUS PUBLICUM, 2015, pp. 135-152.

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■ On Event Detection from Spatial Time series for Urban Traffic Applications
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, S. Michaelis, N. Piatkowski, and M. Stolpe, Eds., Springer International Publishing, 2016, vol. 9580, pp. 221-233.

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■ On Topic Aware Recommendation to Increase Popularity in Microblogging Services
Litou, I., Kalogeraki, V., & Gunopulos, D. (2016, October). On Topic Aware Recommendation to Increase Popularity in Microblogging Services (Short Paper). In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 673-681). Springer International Publishing.

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■ State Detection using Adaptive Human Sensor Sampling
Boutsis, I., Kalogeraki, V., & Gunopulos, D. (2016, September). State Detection Using Adaptive Human Sensor Sampling. In Fourth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2016

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