University of Surrey

Test tubes in the lab Research in the ATI Dance Research

CityPulse: Large Scale Data Analytics Framework for Smart Cities

PUIU, D, Barnaghi, P, TÖNJES, R, Kumper, D, Intizar Ali, M, MILEO, A, Parreira, JX, Fischer, M, KOLOZALI, S, FARAJIDAVAR, N, Gao, F, IGGENA, T, PHAM, T-L, NECHIFOR, C-S, PUSCHMANN, D and FERNANDES, J (2016) CityPulse: Large Scale Data Analytics Framework for Smart Cities IEEE Access, 4.

[img] Text
ACCESS2541999.pdf - Proof
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (3MB)
[img]
Preview
Text (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

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.

Item Type: Article
Subjects : subj_Electronic_Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
AuthorsEmailORCID
PUIU, DUNSPECIFIEDUNSPECIFIED
Barnaghi, PUNSPECIFIEDUNSPECIFIED
TÖNJES, RUNSPECIFIEDUNSPECIFIED
Kumper, DUNSPECIFIEDUNSPECIFIED
Intizar Ali, MUNSPECIFIEDUNSPECIFIED
MILEO, AUNSPECIFIEDUNSPECIFIED
Parreira, JXUNSPECIFIEDUNSPECIFIED
Fischer, MUNSPECIFIEDUNSPECIFIED
KOLOZALI, SUNSPECIFIEDUNSPECIFIED
FARAJIDAVAR, NUNSPECIFIEDUNSPECIFIED
Gao, FUNSPECIFIEDUNSPECIFIED
IGGENA, TUNSPECIFIEDUNSPECIFIED
PHAM, T-LUNSPECIFIEDUNSPECIFIED
NECHIFOR, C-SUNSPECIFIEDUNSPECIFIED
PUSCHMANN, DUNSPECIFIEDUNSPECIFIED
FERNANDES, JUNSPECIFIEDUNSPECIFIED
Date : 5 April 2016
Identification Number : 10.1109/ACCESS.2016.2541999
Copyright Disclaimer : Copyright 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords : Data analytics framework, smart cities
Depositing User : Symplectic Elements
Date Deposited : 13 May 2016 10:39
Last Modified : 13 May 2016 10:39
URI: http://epubs.surrey.ac.uk/id/eprint/810693

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800