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CityPulse: Large Scale Data Analytics Framework for Smart Cities

Authors: 

Dan Puiu, Payam Barnaghi, Ralf Tönjes, Daniel Kümper, Muhammad Intizar Ali, Alessandra Mileo, Josi Xavier Parreira, Marten Fischer, Sefki Kolozali, Nazli Farajidavar, Feng Gao, Torben Iggena, Thu Le Pham, Cosmin-Septimu Nechifor, Daniel Puschmann, Joao Fernandes

Publication Type: 
Refereed Original Article
Abstract: 
Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people’s everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.
Digital Object Identifer (DOI): 
10.1109/ACCESS.2016.2541999
Publication Status: 
Published
Publication Date: 
05/04/2016
Journal: 
IEEE Access
Research Group: 
Institution: 
NUIG
Open access repository: 
Yes