Enhancing urban resilience via a real-time decision support system for smart cities

Sadeeb Ottenburger; Miimu Airaksinen; Isabel Pinto-Seppa; Wolfgang Raskob

doi:10.1109/ICE.2017.8279970

Enhancing urban resilience via a real-time decision support system for smart cities

Sadeeb Ottenburger
, Miimu Airaksinen
, Isabel Pinto-Seppa
, Wolfgang Raskob

Karlsruhe Institute of Technology (KIT)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

9 Citations (Scopus)

Abstract

The emergence of in-memory database technologies may be seen as a groundbreaking development in the segment of data storage and data analytics enabling end-users using real-time applications on top of big data. In this work, we propose a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructures or their components grounding on in-memory database technologies and smart city data sources. A simulation software which utilizes a multi-agent based model for describing the landscape of a smart city's infrastructure or their components incorporating a generic framework for defining disruption scenarios, generates big data which is stored in a database applying in-memory database technologies. According to current urban status data and the type of disruptions, data including made decisions and strategies which are best in the sense of urban resilience is instantly collected from the database.

Original language	English
Title of host publication	2017 International Conference on Engineering, Technology and Innovation
Subtitle of host publication	Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings
Editors	Ricardo Jardim-Goncalves, Joao Pedro Mendonca, Marc Pallot, Alain Zarli, Joao Martins, Maria Marques
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	836-844
Number of pages	9
Volume	2018-January
ISBN (Electronic)	978-1-5386-0774-9
DOIs	https://doi.org/10.1109/ICE.2017.8279970
Publication status	Published - 2 Jul 2017
MoE publication type	A4 Article in a conference publication
Event	23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017 - Madeira Island, Portugal Duration: 27 Jun 2017 → 29 Jun 2017

Conference

Conference	23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017
Country/Territory	Portugal
City	Madeira Island
Period	27/06/17 → 29/06/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Keywords

Critical Infrastructure Protection
Multi-Agent Based Modelling
Real-Time Decision Support System
Smart City
Urban Resilience

Access to Document

10.1109/ICE.2017.8279970

Cite this

Ottenburger, S., Airaksinen, M., Pinto-Seppa, I., & Raskob, W. (2017). Enhancing urban resilience via a real-time decision support system for smart cities. In R. Jardim-Goncalves, J. P. Mendonca, M. Pallot, A. Zarli, J. Martins, & M. Marques (Eds.), 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings (Vol. 2018-January, pp. 836-844). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/ICE.2017.8279970

Ottenburger, Sadeeb ; Airaksinen, Miimu ; Pinto-Seppa, Isabel et al. / Enhancing urban resilience via a real-time decision support system for smart cities. 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. editor / Ricardo Jardim-Goncalves ; Joao Pedro Mendonca ; Marc Pallot ; Alain Zarli ; Joao Martins ; Maria Marques. Vol. 2018-January IEEE Institute of Electrical and Electronic Engineers, 2017. pp. 836-844

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abstract = "The emergence of in-memory database technologies may be seen as a groundbreaking development in the segment of data storage and data analytics enabling end-users using real-time applications on top of big data. In this work, we propose a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructures or their components grounding on in-memory database technologies and smart city data sources. A simulation software which utilizes a multi-agent based model for describing the landscape of a smart city's infrastructure or their components incorporating a generic framework for defining disruption scenarios, generates big data which is stored in a database applying in-memory database technologies. According to current urban status data and the type of disruptions, data including made decisions and strategies which are best in the sense of urban resilience is instantly collected from the database.",

keywords = "Critical Infrastructure Protection, Multi-Agent Based Modelling, Real-Time Decision Support System, Smart City, Urban Resilience",

author = "Sadeeb Ottenburger and Miimu Airaksinen and Isabel Pinto-Seppa and Wolfgang Raskob",

note = "Funding Information: The smart city key performance indicators and framework related work of this paper has been co-funded by the European Commission within the H2020 Programme. (Grant Agreement no: 646440). Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017 ; Conference date: 27-06-2017 Through 29-06-2017",

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Ottenburger, S, Airaksinen, M, Pinto-Seppa, I & Raskob, W 2017, Enhancing urban resilience via a real-time decision support system for smart cities. in R Jardim-Goncalves, JP Mendonca, M Pallot, A Zarli, J Martins & M Marques (eds), 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. vol. 2018-January, IEEE Institute of Electrical and Electronic Engineers, pp. 836-844, 23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017, Madeira Island, Portugal, 27/06/17. https://doi.org/10.1109/ICE.2017.8279970

Enhancing urban resilience via a real-time decision support system for smart cities. / Ottenburger, Sadeeb; Airaksinen, Miimu; Pinto-Seppa, Isabel et al.
2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. ed. / Ricardo Jardim-Goncalves; Joao Pedro Mendonca; Marc Pallot; Alain Zarli; Joao Martins; Maria Marques. Vol. 2018-January IEEE Institute of Electrical and Electronic Engineers, 2017. p. 836-844.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Airaksinen, Miimu

AU - Pinto-Seppa, Isabel

AU - Raskob, Wolfgang

N1 - Funding Information: The smart city key performance indicators and framework related work of this paper has been co-funded by the European Commission within the H2020 Programme. (Grant Agreement no: 646440). Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2017/7/2

Y1 - 2017/7/2

N2 - The emergence of in-memory database technologies may be seen as a groundbreaking development in the segment of data storage and data analytics enabling end-users using real-time applications on top of big data. In this work, we propose a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructures or their components grounding on in-memory database technologies and smart city data sources. A simulation software which utilizes a multi-agent based model for describing the landscape of a smart city's infrastructure or their components incorporating a generic framework for defining disruption scenarios, generates big data which is stored in a database applying in-memory database technologies. According to current urban status data and the type of disruptions, data including made decisions and strategies which are best in the sense of urban resilience is instantly collected from the database.

AB - The emergence of in-memory database technologies may be seen as a groundbreaking development in the segment of data storage and data analytics enabling end-users using real-time applications on top of big data. In this work, we propose a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructures or their components grounding on in-memory database technologies and smart city data sources. A simulation software which utilizes a multi-agent based model for describing the landscape of a smart city's infrastructure or their components incorporating a generic framework for defining disruption scenarios, generates big data which is stored in a database applying in-memory database technologies. According to current urban status data and the type of disruptions, data including made decisions and strategies which are best in the sense of urban resilience is instantly collected from the database.

KW - Critical Infrastructure Protection

KW - Multi-Agent Based Modelling

KW - Real-Time Decision Support System

KW - Smart City

KW - Urban Resilience

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DO - 10.1109/ICE.2017.8279970

M3 - Conference article in proceedings

AN - SCOPUS:85047539131

VL - 2018-January

SP - 836

EP - 844

BT - 2017 International Conference on Engineering, Technology and Innovation

A2 - Jardim-Goncalves, Ricardo

A2 - Mendonca, Joao Pedro

A2 - Pallot, Marc

A2 - Zarli, Alain

A2 - Martins, Joao

A2 - Marques, Maria

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017

Y2 - 27 June 2017 through 29 June 2017

ER -

Ottenburger S, Airaksinen M, Pinto-Seppa I, Raskob W. Enhancing urban resilience via a real-time decision support system for smart cities. In Jardim-Goncalves R, Mendonca JP, Pallot M, Zarli A, Martins J, Marques M, editors, 2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings. Vol. 2018-January. IEEE Institute of Electrical and Electronic Engineers. 2017. p. 836-844 doi: 10.1109/ICE.2017.8279970

Enhancing urban resilience via a real-time decision support system for smart cities

Abstract

Conference

UN SDGs

Keywords

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