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Variety, Veracity, VaLue: Handling the Multiplicity of Urban Sensors

Fact Sheet

Reporting

Results

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Greece

Objective

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.

Field of Science

/natural sciences/computer and information sciences/data science/big data

Programme(s)

H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Topic(s)

ICT-16-2015 - Big data - research

Call for proposal

H2020-ICT-2015

See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Coordinator

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Address

6 Christou Lada Str
10561 Athina

Greece

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 541 250

Website

Contact the organisation

Participants (9)

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TECHNISCHE UNIVERSITAT DORTMUND

Germany

EU Contribution

€ 446 250

TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY

Israel

EU Contribution

€ 440 068,75

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.

Germany

EU Contribution

€ 434 600

IBM IRELAND LIMITED

Ireland

EU Contribution

€ 501 250

AGT GROUP (R&D) GMBH

Germany

EU Contribution

€ 451 250

ORANGE POLSKA SPOLKA AKCYJNA

Poland

EU Contribution

€ 367 500

DUBLIN CITY COUNCIL

Ireland

EU Contribution

€ 212 500

MIASTO STOLECZNE WARSZAWA

Poland

EU Contribution

€ 248 750

POLITECHNIKA WARSZAWSKA

Poland

EU Contribution

€ 356 250

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Greece

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Greece

Periodic Reporting for period 2 - VaVeL (Variety, Veracity, VaLue: Handling the Multiplicity of Urban Sensors)

Reporting period: 2017-06-01 to 2018-11-30

Summary of the context and overall objectives of the project

Technologies that discover knowledge from urban data, such as new techniques for the detection of disastrous events, tracking health issues, monitoring crucial environmental factors, or improving energy efficiency, will have impact in a lot of aspects of the citizens everyday life. The toughest obstacles in using urban data are that such data are heterogeneous, noisy, and unlabeled. In addition, urban data can include massive and high-speed data streams (for example video feeds). Our goal is to provide the tools that will enable the exploitation of big urban data in novel applications that focus on citizen needs and improve the quality of urban life.

The main objectives of VaVeL were the following:
- Objective 1 (Functionality): Address the most critical issues of current (big) data management and stream frameworks regarding coping with emerging urban sensor data.
- Objective 2 (Accessibility): Make European urban data more accessible and easier to utilize by providing a set of black box reusable components that provide access and analyze urban data.
- Objective 3 (Value): Enhance European industries involved in Big Data management and analytics by addressing critical issues and disseminate and exploit knowledge emerged from the project.

The project delivered one final prototype to each one of the cities that participated in the project as an end user. The VaVeL system, installed at the premises of Dublin City and Warsaw City is a fully operational smart city monitoring system focusing on dealing with emergencies, improving public transport as well as providing better services to the citizens. The two cities have evaluated the system and provided feedback for improvement. Both of the cities agreed on the value of VaVeL for managing and getting value out of their data. The prototypes will continue to run after the completion of the project while the two cities have expressed strong interest in continuing research and in developing and deploying product solutions in collaboration with VaVeL partners.

The knowledge and expertise obtained from the project is directly applicable not only to other cities with similar infrastructure, but also to additional applications like environmental modeling, event management, city security, and urban planning. Moreover, the technologies have been demonstrated to scale and allow monitoring situations in a country-wide scale.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

- Research Advancements: The consortium delivered many state-of-the-art techniques, reflected in the 93 publications (42 in the secnd period), the invited talks, and the 6 awards.
- Data Availability and Quality: The consortium focused on that and managed to overcome most of the technical and legal obstacles to make easily accessible multiple sources: traffic volume information from sensors, BUS GPS real time locations, Twitter messages, Tram location information, Crowdsourcing data, weather and pollution data, and CCTV data. The availability of video data was a significant achievement since there were multiple legal and technical difficulties that had to be overcome.
- Exploitation: A detailed plan has been established, including the development of the exploitation committee and an extensive market research.
- Dissemination: With its website, flyer, social media activity, and participation in multiple events and fora, including organizing the MUD workshop at SIGKDD 2018, the VaVeL team had an substantial outreach outcome.
- Development of Realistic Applications. The consortium has made significant progress working in close collaboration with the use partners: Dublin City and the City of Warsaw. Together with the research and industry partners, they developed a detailed set of requirements, resolved data issues, designed the architecture, and built two working prototypes. The two use cases we have developed are complementary and serve as the vehicle to first understand the needs of users and state-holders and subsequently evaluate the proposed solutions in a realistic environment.
- Collaboration: Collaboration among partners were strong during the first period of the project.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The consortium advanced the state-of-the art in distributed computer systems, data mining and machine learning, and Smart City applications with novel techniques that: i) Enable communication and computation efficient monitoring of distributed streams; ii) automatically balance the distributed stream processing system’s load; iii) determine whether and how compression should be applied and minimize the overhead in a Distributed Stream Processing Stream; iv) balance the trade-off between makespan and budget in a MapReduce center and provide cost efficient scheduling of MapReduce workloads across multiple clusters; v) enable reusable elements in situation of multi-level information availability; vi) build and update digital maps from GPS trajectories and identify frequently traveled sub-trajectories; vii) preserve user location and data privacy; viii) provide traffic modeling, route planning, delay prediction, and pollution estimation in urban areas; ix) estimate traffic density and identify emergency vehicles in real-time using video data; x) understand the reachability provided by public transportation in cities; xi) offer context aware services that inform citizens.

Impact.
a. Business and Innovation. The evaluation of pilots validated our approach and demonstrated the demand for our techniques. Through the publication of new open data sources, and through the use of hackathons we promoted the use of open urban data for new applications, thus fostering Smart City Entrepreneurship.
b. Research and Academia. We are confident that the work currently developed by the VaVeL team represents the leading edge on what can be achieved by leveraging both access to real time data and analysis of historical data on traffic related applications. VaVeL partners recognized early on that the next step in advancing the state of the art is to effectively combine both historical and real-time data to provide more accurate longer term analytics to the user. Throughout the duration of the project new technology was created for the industry partners and several students have being trained through their involvement in the research challenges of the project.
c. Social and Environmental Impact. The focus of the work has been to develop techniques that benefit the cities and citizens. Novel analysis techniques that can enhance the situation awareness of the authorities; Context-aware services that can aid in the improvement of the citizens’ daily life in the urban environment; Methods that can predict and estimate pollution. The ultimate result of our efforts is to provide novel tools and techniques for city understanding and eventually improve the city environment, and the quality of life of the citizens.
In addition the novel distributed and real-time data analysis techniques that the consortium introduced are general and powerful enough to enable the development of powerful systems for a wide area of applications, from monitoring and predicting pollution and environmental impact in a country-wide scale to effectively handling massive streaming data sources (such as multiple video streams).
d. Impact to Europe. The technologies developed in VaVeL have been designed to be applicable in many situations and cities with minimum adaptation costs, thus facilitating their deployment in other municipalities and contexts.

Picture of the Warsaw City trip planner application.

Picture of the prototype for Dublin City running.

Vavel dissemination results.

The two cities involved in the use cases.

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Greece

Deliverables

Documents, reports (21)

First Report on System Integration

Report on Citizen and City Services (Traffic, Environment)

Report on City Understanding (Citizen Movement and VA)

Report on Event Prediction

Report on Data Privacy

Report on Requirements and Architecture

Report on Elastic and Resilient Infrastructures

Preliminary Report on Reusable Elements - Lessons Learned

Second Project Dissemination and Exploitation Report

Second Dissemination and Exploitation Report

Report on Data Enrichment Techniques

Final Report on Reusable Elements - Lessons Learned

Second Report on Warsaw Use Case

Report on Novel Architectures for Urban Data Streams

Data Management Plan

Hackathon Report

First Project Dissemination and Exploitation Report

First Dissemination and Exploitation Report

Second Report on Dublin Use Case

First Report on Warsaw Use Case

Second Report on System Integration

Report on Open Urban Data - Lessons Learned

First Report on Dublin Use Case

Websites, patent fillings, videos etc. (1)

Publications

Peer reviewed articles (12)

Exploration and refinement of regression tree models with interactive maps and spatial data transformations

Author(s): Gennady Andrienko, Natalia Andrienko, Alexander Ryumkin, Valery Ryumkin, Gennady Kravchenko, Evegeny Tyabaev, Dmitry Khloptsov, Svetlana Trofimova

Published in: International Journal of Cartography, Issue 2/1, 2016, Page(s) 59-76, ISSN 2372-9333

DOI: 10.1080/23729333.2016.1184551

Dynamic route planning with real-time traffic predictions

Author(s): Thomas Liebig, Nico Piatkowski, Christian Bockermann, Katharina Morik

Published in: Information Systems, Issue 64, 2017, Page(s) 258-265, ISSN 0306-4379

DOI: 10.1016/j.is.2016.01.007

Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data

Author(s): Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jo Wood

Published in: IEEE Transactions on Visualization and Computer Graphics, Issue 23/9, 2017, Page(s) 2120-2136, ISSN 1077-2626

DOI: 10.1109/tvcg.2016.2616404

r -extreme signalling for congestion control

Author(s): Jakub Mareček, Robert Shorten, Jia Yuan Yu

Published in: International Journal of Control, Issue 89/10, 2016, Page(s) 1972-1984, ISSN 0020-7179

DOI: 10.1080/00207179.2016.1146968

Efficient techniques for time-constrained information dissemination using location-based social networks

Author(s): Iouliana Litou, Ioannis Boutsis, Vana Kalogeraki

Published in: Information Systems, Issue 64, 2017, Page(s) 321-349, ISSN 0306-4379

DOI: 10.1016/j.is.2015.12.002

Understanding movement data quality

Author(s): Gennady Andrienko, Natalia Andrienko, Georg Fuchs

Published in: Journal of Location Based Services, Issue 10/1, 2016, Page(s) 31-46, ISSN 1748-9725

DOI: 10.1080/17489725.2016.1169322

Matrix completion under interval uncertainty

Author(s): Jakub Mare?ek, Peter Richt?rik, Martin Tak??

Published in: European Journal of Operational Research, Issue 256/1, 2017, Page(s) 35-43, ISSN 0377-2217

DOI: 10.1016/j.ejor.2016.07.014

REMI: A framework of reusable elements for mining heterogeneous data with missing information

Author(s): Avigdor Gal, Dimitrios Gunopulos, Nikolaos Panagiotou, Nicolo Rivetti, Arik Senderovich, Nikolas Zygouras

Published in: Journal of Intelligent Information Systems, Issue 51/2, 2018, Page(s) 367-388, ISSN 0925-9902

DOI: 10.1007/s10844-018-0524-5

State Transition Graphs for Semantic Analysis of Movement Behaviours

Author(s): Natalia Andrienko, Gennady Andrienko

Published in: Information Visualization, 2017, Page(s) 147387161769284, ISSN 1473-8716

DOI: 10.1177/1473871617692841

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Author(s): Gennady Andrienko, Natalia Andrienko, Wei Chen, Ross Maciejewski, Ye Zhao

Published in: IEEE Transactions on Intelligent Transportation Systems, 2017, Page(s) 1-18, ISSN 1524-9050

DOI: 10.1109/TITS.2017.2683539

A Pareto-based scheduler for exploring cost-performance trade-offs for MapReduce workloads

Author(s): Nikos Zacheilas, Vana Kalogeraki

Published in: EURASIP Journal on Embedded Systems, Issue 2017/1, 2017, ISSN 1687-3963

DOI: 10.1186/s13639-017-0077-7

REMI: A framework of reusable elements for mining heterogeneous data with missing information

Author(s): Avigdor Gal, Dimitrios Gunopulos, Nikolaos Panagiotou, Nicolo Rivetti, Arik Senderovich, Nikolas Zygouras

Published in: Journal of Intelligent Information Systems, Issue 51/2, 2018, Page(s) 367-388, ISSN 0925-9902

DOI: 10.1007/s10844-018-0524-5

Conference proceedings (17)

Dynamic Load Balancing Techniques for Distributed Complex Event Processing Systems

Author(s): Nikos Zacheilas, Nikolas Zygouras, Nikolaos Panagiotou, Vana Kalogeraki, Dimitrios Gunopulos

Published in: Distributed Applications and Interoperable Systems, 2016, Page(s) 174-188

DOI: 10.1007/978-3-319-39577-7_14

IoT Data Analytics – A key enabler for the Growth of Smart Cities

Author(s): Manuel Görtz, Martin Strohbach, Markus Schlattmann

Published in: SocietyByte – Wissenschaftsmagazin des BFH-Zentrums Digital Society, 2017

AI-based analysis methods in spatio-temporal data mining

Author(s): Thomas Liebig

Published in: AI: Philosophy, Geoinformatics & Law, 2015

State Detection Using Adaptive Human Sensor Sampling

Author(s): Boutsis Ioannis, Kalogeraki Vana, Gunopulos Dimitrios

Published in: Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2016), 2016

Dynamic Map Update of Non-Static Facility Logistics Environment with a Multi-Robot System

Author(s): Nayabrasul Shaik, Thomas Liebig, Christopher Kirsch, Heinrich Muller

Published in: Proceedings of the 40th German Conference on Artificial Intelligence, 2017

DIsCO: DynamIc Data COmpression in Distributed Stream Processing Systems

Author(s): Nikos Zacheilas, Vana Kalogeraki

Published in: Proceedings of the 17th Distributed Applications and Interoperable Systems (DAIS) 2017, Held as Part of the 12th International Federated Conference on Distributed Computing Techniques, DisCoTec 2017, 2017, Page(s) 19-33

DOI: 10.1007/978-3-319-59665-5_2

Real-time Public Transport Delay Prediction for Situation-aware Routing

Author(s): Lukas Heppe, Thomas Liebig

Published in: Proceedings of the 40th German Conference on Artificial Intelligence, 2017

Distributionally Robust Optimization in Congestion Control

Author(s): Jakub Mareček and Robert Shorten and Jia Yuan Yu

Published in: arXiv, 2016

An Active Learning Framework Incorporating User Input For Mining Urban Data

Author(s): Zygouras Nikolas, Panagiotou Nikolaos, Zacheilas Nikos, Boutsis Ioannis, Kalogeraki Vana, Gunopulos Dimitrios

Published in: The 5th International Workshop on Urban Computing, 2016

Intelligent Urban Data Monitoring for Smart Cities

Author(s): Nikolaos Panagiotou, Nikolas Zygouras, Ioannis Katakis, Dimitrios Gunopulos, Nikos Zacheilas, Ioannis Boutsis, Vana Kalogeraki, Stephen Lynch, Brendan O?Brien

Published in: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, 2016, Page(s) 177-192

DOI: 10.1007/978-3-319-46131-1_23

On Topic Aware Recommendation to Increase Popularity in Microblogging Services

Author(s): Iouliana Litou, Vana Kalogeraki, Dimitrios Gunopulos

Published in: "OTM Confederated International Conferences"" On the Move to Meaningful Internet Systems", 2016, Page(s) 673-681

DOI: 10.1007/978-3-319-48472-3_40

Online Problems in Timetabling: Bus Priority at Signalised Junctions

Author(s): Randall Cogill, Jakub Marecek, Martin Mevissen, Hana Rudova

Published in: PATAT, the International Conference on the Practice and Theory of Automated Timetabling, 2016

Clustering of Mobile Subscriber’s Location Statistics for Travel Demand Zones Diversity

Author(s): Marcin Luckner, Aneta Ros?an, Izabela Krzemi?ska, Jaros?aw Legierski, Robert Kunicki

Published in: Computer Information Systems and Industrial Management. CISIM 2017, 2017, Page(s) 315-326

DOI: 10.1007/978-3-319-59105-6_27

On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning

Author(s): Thomas Liebig, Maurice Sotzny

Published in: Proceedings of the 13th International Conference on Spatial Information Theory {COSIT}, 2017

Travel Time Prediction for Trams in Warsaw

Author(s): Adam Zychowski, Konstanty Junosza-Szaniawski, Aleksander Kosicki

Published in: 10th International Conference on Computer Recognition Systems CORES 2017., 2017, Page(s) 53-62

DOI: 10.1007/978-3-319-59162-9_6

First Story Detection using Entities and Relations

Author(s): Nikolaos Panagiotou, Cem Akkaya, Kostas Tsioutsiouliklis, Vana Kalogeraki, Dimitrios Gunopulos

Published in: (COLING) 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11-16, 2016, Osaka, Japan, 2016

Lessons Learnt from the analysis of a bike sharing system, , ,

Author(s): Dimitrios Tomaras, Ioannis Boutsis, Vana Kalogeraki

Published in: 10th International PETRA 2017, 2017

Book chapters (1)

Smart navigation - chances, risk and challenges

Author(s): Thomas Liebig

Published in: Navigation and Earth Observation - Law & Technology, 2017

Non-peer reviewed articles (2)

Efficient and timely misinformation blocking under varying cost constraints

Author(s): Iouliana Litou, Vana Kalogeraki, Ioannis Katakis, Dimitrios Gunopulos

Published in: Online Social Networks and Media, Issue 2, 2017, Page(s) 19-31, ISSN 2468-6964

DOI: 10.1016/j.osnem.2017.07.001

Efficient and timely misinformation blocking under varying cost constraints

Author(s): Iouliana Litou, Vana Kalogeraki, Ioannis Katakis, Dimitrios Gunopulos

Published in: Online Social Networks and Media, Issue 2, 2017, Page(s) 19-31, ISSN 2468-6964

DOI: 10.1016/j.osnem.2017.07.001

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

Greece

Project information

VaVeL

Grant agreement ID: 688380

Project website

Start date

1 December 2015
End date

30 November 2018

Funded under:

H2020-EU.2.1.1.

Overall budget:

€ 3 999 668,75
EU contribution

€ 3 999 668,75

Coordinated by:

ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON

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Last update: 11 December 2019

Record number: 199181

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Periodic Reporting for period 2 - VaVeL (Variety, Veracity, VaLue: Handling the Multiplicity of Urban Sensors)

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