Back to Top

Publications

■ Maximizing Determinism in Stream Processing Under Latency Constraints
Maximizing Determinism in Stream Processing Under Latency Constraints, Nikos Zacheilas, Vana Kalogeraki, Yiannis Nikolakopoulos, Vincenzo Gulisano, Marina Papatriantafilou, Philippas Tsigas, ACM DEBS, Barcelona, Spain, June 19 - 23, 2017 (received best paper award)

Get the paper
■ DIsCO: DynamIc Data COmpression in Distributed Stream Processing Systems
DIsCO: DynamIc Data COmpression in Distributed Stream Processing Systems, Zacheilas Nikos, Vana Kalogeraki, DAIS 2017, Neuchâtel, Switzerland, June 19 - 22, 2017

Get the paper
■ A Framework for Efficient Energy Scheduling of Spark Workloads
A Framework for Efficient Energy Scheduling of Spark Workloads, Stathis Maroulis, Nikos Zacheilas, Vana Kalogeraki , IEEE ICDCS 2017, Atlanta, GA, USA, June 5 - 8, 2017 (poster)

Get the paper
■ Pythia: A System for Online Topic Discovery of Social Media Posts
Pythia: A System for Online Topic Discovery of Social Media Posts, Iouliana Litou, Vana Kalogeraki, IEEE ICDCS 2017, Atlanta, GA, USA, June 5 - 8, 2017 (Demo)

Get the paper
■ Influence Maximization in a Many Cascades World
Influence Maximization in a Many Cascades World, Iouliana Litou, Vana Kalogeraki, Dimitrios Gunopulos, IEEE ICDCS, Atlanta, GA, USA, June 5 - 8, 2017

Get the paper
■ Lessons Learnt from the analysis of a bike sharing system
Lessons Learnt from the analysis of a bike sharing system, Dimitrios Tomaras, Ioannis Boutsis, Vana Kalogeraki, 10th International PETRA 2017, Rhodes, Greece, June 21 - 23, 2017

Get the paper
■ Dynamic map update of non-static facility logistics environment with a multi-robot system
N. Shaik, T. Liebig, C. Kirsch, and H. Müller, “Dynamic map update of non-static facility logistics environment with a multi-robot system,” in Proceedings of the 40th German Conference on Artificial Intelligence, Springer Berlin Heidelberg, 2017, p. (accepted).

Get the paper
■ Real-time Public Transport Delay Prediction for Situation-aware Routing
L. Heppe and T. Liebig, “Real-time Public Transport Delay Prediction for Situation-aware Routing,” in Proceedings of the 40th German Conference on Artificial Intelligence, Springer Berlin Heidelberg, 2017, p. (accepted).

Get the paper
■ Combining Stream Mining and Neural Networks for Short Term Delay Prediction
M. Grzenda, K. Kwasiborska, T. Zaremba: Combining Stream Mining and Neural Networks for Short Term Delay Prediction. Accepted in International Conference on Soft Computing Models in Industrial and Environmental Applications, Leon, Spain. SOCO’17. 6th-8th September 2017
The location readings from GPS-based devices in public transport vehicles are received with latency caused by partly irregular data transmission; such latency hinders real-time identification of delays. Fig. 1 shows the histogram of sample latencies in location data acquisition. In this work we proposed a hybrid method combining stream mining (Hoeffding tree) and batch learning (multilayer perceptron) increasing the accuracy of the prediction of tram delay statuses. Such predicted statuses can be used as a temporary replacement of ground truth tram delay statuses, not available in real-time due to the aforementioned data transmission latencies.
Get the paper
■ REMI, Reusable Elements for Multi-Level Information Availability
A. Senderovich, N. Rivetti, A. Gal, N. Panagiotou, I. Katakis, D. Gunopulos, V. Kalogeraki: REMI, Reusable Elements for Multi-Level Information Availability. Accepted in DEBS 2017
We provide a framework that enables data solutions to run on less-than-perfect sources of information. We name our solution REMI for Reusable Elements for Missing Information. Our approach comprises of elements that can adapt themselves, by design, to the changing levels of data availability. The core of the REMI framework is the layered stack architecture, equipped with data processing units with two additional computational elements, namely DARE (data enrichment) and GRADE (graceful degradation).
Get the paper
■ 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).

Get the paper
■ 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).

Get the paper
■ 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.

Get the paper
■ 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.

Get the paper
■ 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.
Get the paper
■ 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.
Get the paper
■ 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.
Get the paper
■ 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.
Get the paper
■ 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.
Get the paper

Pages