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■ Accurate prediction of queue length in public service offices on the basis of Open Urban Data APIs
Piotr Wawrzyniak, Jaroslaw Legierski , "Accurate prediction of queue length in public service offices on the basis of Open Urban Data APIs", Federated Conference on Computer Science and Information Systems FedCSIS 2016, Gdańsk, Poland.

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■ Mining Hidden Constrained Streams in Practice: Informed Search in Dynamic Filter Spaces
N. Panagiotou, I. Katakis, D. Gunopulos, V. Kalogeraki, E. Daly, J. Yuan Yu, Brendan O’ Brien, The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, CA, USA, 18 – 21 August, 2016

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■ Online Problems in Timetabling: Bus Priority at Signalised Junctions
Randall Cogill, Jakub Marecek, Martin Mevissen, Hana Rudova, " Online Problems in Timetabling: Bus Priority at Signalised Junctions" PATAT, Udine, Italy, August 2016

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■ Matrix Completion under Interval Uncertainty
Mareček, J., Richtárik, P., & Takáč, M. (2017). Matrix completion under interval uncertainty. European Journal of Operational Research, 256(1), 35-43.
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We show that alternating-direction parallel coordinate-descent method for the problem outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes.
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■ ICU: A tool for Intent Filtering on Android devices
Bakopoulou, E., Thanopoulos, P., Boutsis, I., & Kalogeraki, V. (2016, June). ICU: A tool for Intent Filtering on Android devices. In Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments (p. 79). ACM.

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■ Complete Coverage Path Planning for arbitrary number of Unmanned Aerial Vehicles
D. Dedousis, V. Kalogeraki, PETRA 2016, Corfu Island, June-July 2016

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■ Reliable Crowdsourced Event Detection in Smart Cities
Boutsis, I., Kalogeraki, V., & Guno, D. (2016, April). Reliable crowdsourced event detection in smartcities. In Science of Smart City Operations and Platforms Engineering (SCOPE) in partnership with Global City Teams Challenge (GCTC)(SCOPE-GCTC), 2016 1st International Workshop on (pp. 1-6). IEEE.

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■ Pareto-based Scheduling of MapReduce Workloads
Zacheilas, N., & Kalogeraki, V. (2016, May). Pareto-Based Scheduling of MapReduce Workloads. In Real-Time Distributed Computing (ISORC), 2016 IEEE 19th International Symposium on (pp. 174-181). IEEE.

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■ ChEsS: Cost-Effective Scheduling across Multiple Heterogeneous MapReduce Clusters
Zacheilas, N., & Kalogeraki, V. (2016, July). ChEsS: Cost-Effective Scheduling across multiple heterogeneous mapreduce clusters. In Autonomic Computing (ICAC), 2016 IEEE International Conference on (pp. 65-74). IEEE.

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■ Using Human Social Sensors for Robust Event Location Detection
Boutsis, I., & Kalogeraki, V. (2016, May). Using Human Social Sensors for Robust Event Location Detection. In Distributed Computing in Sensor Systems (DCOSS), 2016 International Conference on (pp. 105-107). IEEE.

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■ A fast and efficient entity resolution approach for preserving privacy in mobile data
Boutsis, I., & Kalogeraki, V. (2016, June). A fast and efficient entity resolution approach for preserving privacy in mobile data. In Big Data (BigData Congress), 2016 IEEE International Congress on (pp. 173-180). IEEE, San Francisco, CA.

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■ Real-Time and Cost-Effective Limitation of Misinformation Propagation
Litou, I., Kalogeraki, V., Katakis, I., & Gunopulos, D. (2016, June). Real-Time and Cost-Effective Limitation of Misinformation Propagation. In Mobile Data Management (MDM), 2016 17th IEEE International Conference on (Vol. 1, pp. 158-163). IEEE.

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■ Dynamic Load Balancing Techniques for Distributed Complex Event Processing Systems
Zacheilas, N., Zygouras, N., Panagiotou, N., Kalogeraki, V., & Gunopulos, D. (2016). Dynamic Load Balancing Techniques for Distributed Complex Event Processing Systems. In Distributed Applications and Interoperable Systems (pp. 174-188). Springer International Publishing.

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■ Understanding movement data quality
Andrienko, G., Andrienko, N., & Fuchs, G. (2016). Understanding movement data quality. Journal of location Based services, 10(1), 31-46, DOI: 10.1080/17489725.2016.1169322
This paper reviews the key properties of movement data and, on their basis, create a typology of possible data quality problems and suggest approaches to identify these types of problems. The proposed methods are essential for the overall success of analyses and predictions in all tasks of VaVeL.
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■ Distributionally Robust Optimization in Congestion Control
Jakub Mareček, Robert Shorten, Jia Yuan Yu, "Distributionally Robust Optimization in Congestion Control", ITS European Congress 2016
The model of Marecek et al. [arXiv:1406.7639, Int. J. Control 88(10), 2015] is extended to consider uncertainty in the response of a driver to an interval provided per route. Specifically, it is suggested that one can optimise over all distributions of a random variable associated with the driver's response with the first two moments fixed, and for each route, over the sub-intervals within the minimum and maximum in a certain number of previous realisations of the travel time per the route.
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■ Pricing Vehicle Sharing with Proximity Information
Mareček, J., Shorten, R., & Yu, J. Y. (2016, March). Pricing vehicle sharing with proximity information. In Big Data and Smart City (ICBDSC), 2016 3rd MEC International Conference on (pp. 1-7). IEEE.
For vehicle sharing schemes, where drop-off positions are not fixed, we propose a pricing scheme, where the price depends in part on the distance between where a vehicle is being dropped off and where the closest shared vehicle is parked. Under certain restrictive assumptions, we show that this pricing leads to a socially optimal spread of the vehicles within a region.
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■ r-Extreme Signalling for Congestion Control
Jakub Mareček , Robert Shorten , Jia Yuan Yu, "r-Extreme Signalling for Congestion Control", International Journal of Control, 2016
In many smart-city applications, congestion arises in part due to the nature of signals received by individuals from a central authority. We study the setting, where a central authority broadcasts an interval per resource, as obtained by taking the minima and maxima of costs observed within a time window of length r, and show that the resulting distribution of agents across resources also converges in distribution, under plausible assumptions about the evolution of the population over time.
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