Browse DORAS
Browse Theses
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

Automatic configuration of smart city applications for user-centric decision support

Pham, Thu-Le and Germano, Stefano and Mileo, Alessandra and Kuemper , Daniel and Muhammad, Intizar Ali (2017) Automatic configuration of smart city applications for user-centric decision support. In: Workshop on IoT Infrastructures and Data Analytics for Smart Cities, 7-9 Mar 2017, Paris, France. ISBN 2472-8144

Full text available as:

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Smart city applications in the Big Data era require not only techniques dedicated to dynamicity handling, but also the ability to take into account contextual information, user preferences and requirements, and real-time events to provide optimal solutions and automatic configuration for the end user. In this paper, we present a specific functionality in the design and implementation of a declarative decision support component that exploits contextual information, user preferences and requirements to automatically provide optimal configurations of smart city applications. The key property of user-centricity of our approach is achieved by enabling users to declaratively specify constraints and preferences on the solutions provided by the smart city application through the Decision Support component, and automatically map these constraints and preferences to provide optimal responses targeting user needs. We showcase the effectiveness and flexibility of our solution in two real usecase scenarios: a multimodal travel planner and a mobile parking application. All the components and algorithms described in this paper have been defined and implemented as part of the Smart City Framework CityPulse (

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:Smart cities; Real-time systems; Data acquisition; Semantics; Decision Support
Subjects:Computer Science > Computer networks
Computer Science > Information technology
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in:20th Conference on Innovations in Clouds, Internet and Networks (ICIN). ICIN (16809174). IEEE. ISBN 2472-8144
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:EU Citypulse grant No.603095., Science Foundation Ireland No. SFI/12/RC/2289
ID Code:21770
Deposited On:03 May 2017 10:57 by Alessandra Mileo. Last Modified 06 Jun 2017 12:12

Download statistics

Archive Staff Only: edit this record