Applications of big data to smart cities
Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the perform...
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| Published in | Journal of internet services and applications Vol. 6; no. 1; p. 1 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.12.2015
Sociedade Brasileira de Computação |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1867-4828 1869-0238 |
| DOI | 10.1186/s13174-015-0041-5 |
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| Abstract | Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology. |
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| AbstractList | Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology. |
| ArticleNumber | 25 |
| Author | Al Neyadi, Hind Mohamed, Nader Al Nuaimi, Eiman Al-Jaroodi, Jameela |
| Author_xml | – sequence: 1 givenname: Eiman surname: Al Nuaimi fullname: Al Nuaimi, Eiman organization: College of Information Technology, UAE University – sequence: 2 givenname: Hind surname: Al Neyadi fullname: Al Neyadi, Hind organization: College of Information Technology, UAE University – sequence: 3 givenname: Nader surname: Mohamed fullname: Mohamed, Nader email: nader@middleware-tech.net organization: Middleware Technologies Labs – sequence: 4 givenname: Jameela surname: Al-Jaroodi fullname: Al-Jaroodi, Jameela organization: Department of Engineering, Robert Morris University |
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| PublicationTitle | Journal of internet services and applications |
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| PublicationYear | 2015 |
| Publisher | Springer London Sociedade Brasileira de Computação |
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| References | Middleton A, Solutions PDLR. Hpcc systems: Introduction to hpcc (high-performance computing cluster). White paper, LexisNexis Risk Solutions; 2011. TeneOPolonetskyJBig data for all: Privacy and user control in the age of analyticsNw J Tech Intell Prop201211xxvii Business analytics from basics to value, Gartner, Retrieved 4 May 15, Published on Jun 10, 2014, available athttp://www.slideshare.net/sucesuminas/business-analytics-from-basics-to-value. XiaofengMXiangCBig data management: concepts, techniques and challenges [J]J Comput Res Dev2013198 KramersAHöjerMLövehagenNWangelJSmart sustainable cities–Exploring ICT solutions for reduced energy use in citiesEnviron Model Software201456526210.1016/j.envsoft.2013.12.019 Yin J, Sharma P, Gorton I, Akyoli, B. Large-Scale Data Challenges in Future Power Grids. In Service Oriented System Engineering (SOSE), 2013 IEEE 7th International Symposium on IEEE; 2013. pp. 324–328. Biem A, Bouillet E, Feng H, Ranganathan A, Riabov A, Verscheure O, Moran C. Ibminfosphere streams for scalable, real-time, intelligent transportation services. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data ACM; 2010. pp. 1093–1104. NeirottiPDe MarcoACaglianoACManganoGScorranoFCurrent trends in Smart City initiatives: Some stylised factsCities201438253610.1016/j.cities.2013.12.010 Chaudhuri S. What next?: a half-dozen data management research goals for big data and the cloud. In Proceedings of the 31st symposium on Principles of Database Systems. ACM; 2012. pp. 1–4. Tantatsanawong P, Kawtrakul A, Lertwipatrakul W. Enabling future education with smart services. In SRII Global Conference (SRII), 2011 Annual IEEE; 2011. pp. 550–556. KimGHTrimiSChungJHBig-data applications in the government sectorCommun ACM2014573788510.1145/2500873 WuXZhuXWuGQDingWData mining with big dataIEEE Trans Knowl Data Eng20142619710710.1109/TKDE.2013.109 Khan M, Uddin MF, Gupta N. Seven V’s of Big Data understanding Big Data to extract value. In American Society for Engineering Education (ASEE Zone 1), 2014 Zone 1 Conference of the IEEE; 2014. pp. 1–5. Al-HaderMRodziAThe smart city infrastructure development & monitoringTheor Empir Res Urban Manage2009428794 Chourabi H, Nam T, Walker S, Gil-Garcia JR, Mellouli S, Nahon K, Scholl HJ. Understanding smart cities: An integrative framework. In System Science (HICSS), 2012 45th Hawaii International Conference on IEEE; 2012. pp. 2289–2297. Mohamed N, Al-Jaroodi J, “Real-time big data analytics: Applications and challenges,” High Performance Computing & Simulation (HPCS), 2014 International Conference on, vol., no., 2014. pp. 305,310. Borkar V, Carey MJ, Li C. Inside Big Data management: ogres, onions, or parfaits?. In Proceedings of the 15th International Conference on Extending Database Technology. ACM; 2012. pp. 3–14. FanWBifetAMining big data: current status, and forecast to the futureACM SIGKDD Explor Newsl2013142151289.9200210.1145/2481244.2481246 U.S. Department of Energy, “Smart Grid / Department of Energy,” Web: http://energy.gov/oe/technology-development/smart-grid, RetrievedSep. 23, 2015. Ji C, Li Y, Qiu W, Awada U, Li K. Big data processing in cloud computing environments. In Pervasive Systems, Algorithms and Networks (ISPAN), 2012 12th International Symposium on IEEE; 2012. pp. 17–23. Michalik P, Stofa J, Zolotova I. Concept definition for Big Data architecture in the education system. In Applied Machine Intelligence and Informatics (SAMI), 2014 IEEE 12th International Symposium on 2014. pp. 331–334. BattyMBig data, smart cities and city planningDialogues Hum Geog20133327427966686210.1177/2043820613513390 DittrichJQuiané-RuizJAEfficient big data processing in Hadoop MapReduceProc VLDB Endowment20125122014201510.14778/2367502.2367562 Khan Z, Anjum A, Kiani SL. Cloud Based Big Data Analytics for Smart Future Cities. In Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. IEEE Computer Society; 2013. pp. 381–386. Pantelis K, Aija L. Understanding the value of (big) data. In Big Data, 2013 IEEE International Conference on IEEE; 2013. pp. 38–42. KitchinRThe real-time city? Big data and smart urbanismGeoJournal201479111410.1007/s10708-013-9516-8 Lee CH, Birch D, Wu C, Silva D, Tsinalis O, Li Y, Guo Y. Building a generic platform for big sensor data application. In Big Data, 2013 IEEE International Conference on IEEE; 2013. pp. 94–102. West DM. Big Data for Education: Data Mining, Data Analytics, and Web Dashboards. Governance Studies at Brookings. 2012. Available athttp://www.brookings.edu/~/media/Research/Files/Papers/2012/9/04%20education%20technology%20west/04%20education%20technology%20west.pdf AguileraGGalanJLCamposJCRodríguezPAn Accelerated-Time Simulation for Traffic Flow in a Smart CityFEMTEC2013201326 Bertot JC, Choi H. Big data and e-government: issues, policies, and recommendations. In Proceedings of the 14th Annual International Conference on Digital Government Research. ACM; 2013. pp. 1–10. VilajosanaILlosaJMartinezBDomingo-PrietoMAnglesAVilajosanaXBootstrapping smart cities through a self-sustainable model based on big data flowsCommun Mag, IEEE201351612813410.1109/MCOM.2013.6525605 Townsend AM 2013. Smart cities: big data, civic hackers, and the quest for a new utopia. WW Norton & Company. AlexandrovABergmannREwenSFreytagJCHueskeFHeiseAWarnekeDThe Stratosphere platform for big data analyticsVLDB J201423693996410.1007/s00778-014-0357-y Marsh O, Maurov-Horvat L, Stevenson O. Big Data and Education: What’s the Big Idea?. UCL Policy Briefing. 2014. Available at https://www.ucl.ac.uk/public-policy/public-policy-briefings/big_data_briefing_final.pdf Su K, Li J, Fu H. Smart city and the applications. In Electronics, Communications and Control (ICECC), 2011 International Conference on IEEE; 2011. pp. 1028–1031. A Kramers (41_CR11) 2014; 56 P Neirotti (41_CR12) 2014; 38 41_CR29 GH Kim (41_CR23) 2014; 57 41_CR24 41_CR22 41_CR21 41_CR27 41_CR26 M Xiaofeng (41_CR25) 2013; 1 I Vilajosana (41_CR6) 2013; 51 41_CR20 A Alexandrov (41_CR30) 2014; 23 R Kitchin (41_CR3) 2014; 79 W Fan (41_CR8) 2013; 14 41_CR19 41_CR18 J Dittrich (41_CR28) 2012; 5 41_CR13 41_CR35 41_CR10 41_CR32 41_CR17 41_CR15 X Wu (41_CR33) 2014; 26 41_CR14 M Al-Hader (41_CR9) 2009; 4 41_CR31 M Batty (41_CR5) 2013; 3 41_CR4 41_CR1 41_CR2 41_CR7 G Aguilera (41_CR16) 2013; 2013 O Tene (41_CR34) 2012; 11 |
| References_xml | – reference: U.S. Department of Energy, “Smart Grid / Department of Energy,” Web: http://energy.gov/oe/technology-development/smart-grid, RetrievedSep. 23, 2015. – reference: BattyMBig data, smart cities and city planningDialogues Hum Geog20133327427966686210.1177/2043820613513390 – reference: Su K, Li J, Fu H. Smart city and the applications. In Electronics, Communications and Control (ICECC), 2011 International Conference on IEEE; 2011. pp. 1028–1031. – reference: West DM. Big Data for Education: Data Mining, Data Analytics, and Web Dashboards. Governance Studies at Brookings. 2012. Available athttp://www.brookings.edu/~/media/Research/Files/Papers/2012/9/04%20education%20technology%20west/04%20education%20technology%20west.pdf – reference: AlexandrovABergmannREwenSFreytagJCHueskeFHeiseAWarnekeDThe Stratosphere platform for big data analyticsVLDB J201423693996410.1007/s00778-014-0357-y – reference: TeneOPolonetskyJBig data for all: Privacy and user control in the age of analyticsNw J Tech Intell Prop201211xxvii – reference: Middleton A, Solutions PDLR. Hpcc systems: Introduction to hpcc (high-performance computing cluster). White paper, LexisNexis Risk Solutions; 2011. – reference: Townsend AM 2013. Smart cities: big data, civic hackers, and the quest for a new utopia. WW Norton & Company. – reference: AguileraGGalanJLCamposJCRodríguezPAn Accelerated-Time Simulation for Traffic Flow in a Smart CityFEMTEC2013201326 – reference: Borkar V, Carey MJ, Li C. Inside Big Data management: ogres, onions, or parfaits?. In Proceedings of the 15th International Conference on Extending Database Technology. ACM; 2012. pp. 3–14. – reference: Tantatsanawong P, Kawtrakul A, Lertwipatrakul W. Enabling future education with smart services. In SRII Global Conference (SRII), 2011 Annual IEEE; 2011. pp. 550–556. – reference: Business analytics from basics to value, Gartner, Retrieved 4 May 15, Published on Jun 10, 2014, available athttp://www.slideshare.net/sucesuminas/business-analytics-from-basics-to-value. – reference: Lee CH, Birch D, Wu C, Silva D, Tsinalis O, Li Y, Guo Y. Building a generic platform for big sensor data application. In Big Data, 2013 IEEE International Conference on IEEE; 2013. pp. 94–102. – reference: WuXZhuXWuGQDingWData mining with big dataIEEE Trans Knowl Data Eng20142619710710.1109/TKDE.2013.109 – reference: Pantelis K, Aija L. Understanding the value of (big) data. In Big Data, 2013 IEEE International Conference on IEEE; 2013. pp. 38–42. – reference: XiaofengMXiangCBig data management: concepts, techniques and challenges [J]J Comput Res Dev2013198 – reference: Marsh O, Maurov-Horvat L, Stevenson O. Big Data and Education: What’s the Big Idea?. UCL Policy Briefing. 2014. Available at https://www.ucl.ac.uk/public-policy/public-policy-briefings/big_data_briefing_final.pdf – reference: Khan M, Uddin MF, Gupta N. Seven V’s of Big Data understanding Big Data to extract value. In American Society for Engineering Education (ASEE Zone 1), 2014 Zone 1 Conference of the IEEE; 2014. pp. 1–5. – reference: KimGHTrimiSChungJHBig-data applications in the government sectorCommun ACM2014573788510.1145/2500873 – reference: Chourabi H, Nam T, Walker S, Gil-Garcia JR, Mellouli S, Nahon K, Scholl HJ. Understanding smart cities: An integrative framework. In System Science (HICSS), 2012 45th Hawaii International Conference on IEEE; 2012. pp. 2289–2297. – reference: Michalik P, Stofa J, Zolotova I. Concept definition for Big Data architecture in the education system. In Applied Machine Intelligence and Informatics (SAMI), 2014 IEEE 12th International Symposium on 2014. pp. 331–334. – reference: KramersAHöjerMLövehagenNWangelJSmart sustainable cities–Exploring ICT solutions for reduced energy use in citiesEnviron Model Software201456526210.1016/j.envsoft.2013.12.019 – reference: Chaudhuri S. What next?: a half-dozen data management research goals for big data and the cloud. In Proceedings of the 31st symposium on Principles of Database Systems. ACM; 2012. pp. 1–4. – reference: Ji C, Li Y, Qiu W, Awada U, Li K. Big data processing in cloud computing environments. In Pervasive Systems, Algorithms and Networks (ISPAN), 2012 12th International Symposium on IEEE; 2012. pp. 17–23. – reference: KitchinRThe real-time city? Big data and smart urbanismGeoJournal201479111410.1007/s10708-013-9516-8 – reference: FanWBifetAMining big data: current status, and forecast to the futureACM SIGKDD Explor Newsl2013142151289.9200210.1145/2481244.2481246 – reference: Mohamed N, Al-Jaroodi J, “Real-time big data analytics: Applications and challenges,” High Performance Computing & Simulation (HPCS), 2014 International Conference on, vol., no., 2014. pp. 305,310. – reference: Yin J, Sharma P, Gorton I, Akyoli, B. Large-Scale Data Challenges in Future Power Grids. In Service Oriented System Engineering (SOSE), 2013 IEEE 7th International Symposium on IEEE; 2013. pp. 324–328. – reference: Al-HaderMRodziAThe smart city infrastructure development & monitoringTheor Empir Res Urban Manage2009428794 – reference: NeirottiPDe MarcoACaglianoACManganoGScorranoFCurrent trends in Smart City initiatives: Some stylised factsCities201438253610.1016/j.cities.2013.12.010 – reference: DittrichJQuiané-RuizJAEfficient big data processing in Hadoop MapReduceProc VLDB Endowment20125122014201510.14778/2367502.2367562 – reference: Bertot JC, Choi H. Big data and e-government: issues, policies, and recommendations. In Proceedings of the 14th Annual International Conference on Digital Government Research. ACM; 2013. pp. 1–10. – reference: Biem A, Bouillet E, Feng H, Ranganathan A, Riabov A, Verscheure O, Moran C. Ibminfosphere streams for scalable, real-time, intelligent transportation services. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data ACM; 2010. pp. 1093–1104. – reference: VilajosanaILlosaJMartinezBDomingo-PrietoMAnglesAVilajosanaXBootstrapping smart cities through a self-sustainable model based on big data flowsCommun Mag, IEEE201351612813410.1109/MCOM.2013.6525605 – reference: Khan Z, Anjum A, Kiani SL. Cloud Based Big Data Analytics for Smart Future Cities. In Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. IEEE Computer Society; 2013. pp. 381–386. – ident: 41_CR26 doi: 10.1145/2247596.2247598 – volume: 11 start-page: xxvii year: 2012 ident: 41_CR34 publication-title: Nw J Tech Intell Prop – volume: 3 start-page: 274 issue: 3 year: 2013 ident: 41_CR5 publication-title: Dialogues Hum Geog doi: 10.1177/2043820613513390 – volume: 4 start-page: 87 issue: 2 year: 2009 ident: 41_CR9 publication-title: Theor Empir Res Urban Manage – ident: 41_CR17 – volume: 51 start-page: 128 issue: 6 year: 2013 ident: 41_CR6 publication-title: Commun Mag, IEEE doi: 10.1109/MCOM.2013.6525605 – ident: 41_CR35 – ident: 41_CR32 doi: 10.1109/I-SPAN.2012.9 – ident: 41_CR14 – ident: 41_CR13 doi: 10.1109/SRII.2011.63 – ident: 41_CR1 doi: 10.1109/BigData.2013.6691691 – ident: 41_CR19 doi: 10.1109/HPCSim.2014.6903700 – volume: 26 start-page: 97 issue: 1 year: 2014 ident: 41_CR33 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2013.109 – ident: 41_CR10 doi: 10.1145/2479724.2479730 – volume: 57 start-page: 78 issue: 3 year: 2014 ident: 41_CR23 publication-title: Commun ACM doi: 10.1145/2500873 – volume: 23 start-page: 939 issue: 6 year: 2014 ident: 41_CR30 publication-title: VLDB J doi: 10.1007/s00778-014-0357-y – ident: 41_CR7 doi: 10.1109/SAMI.2014.6822433 – ident: 41_CR24 doi: 10.1109/HICSS.2012.615 – volume: 1 start-page: 98 year: 2013 ident: 41_CR25 publication-title: J Comput Res Dev – volume: 2013 start-page: 26 year: 2013 ident: 41_CR16 publication-title: FEMTEC – ident: 41_CR20 doi: 10.1109/ASEEZone1.2014.6820689 – volume: 38 start-page: 25 year: 2014 ident: 41_CR12 publication-title: Cities doi: 10.1016/j.cities.2013.12.010 – ident: 41_CR4 – ident: 41_CR15 – volume: 5 start-page: 2014 issue: 12 year: 2012 ident: 41_CR28 publication-title: Proc VLDB Endowment doi: 10.14778/2367502.2367562 – volume: 14 start-page: 1 issue: 2 year: 2013 ident: 41_CR8 publication-title: ACM SIGKDD Explor Newsl doi: 10.1145/2481244.2481246 – ident: 41_CR31 doi: 10.1145/1807167.1807291 – ident: 41_CR27 doi: 10.1145/2213556.2213558 – ident: 41_CR18 doi: 10.1109/SOSE.2013.71 – ident: 41_CR2 doi: 10.1109/UCC.2013.77 – volume: 56 start-page: 52 year: 2014 ident: 41_CR11 publication-title: Environ Model Software doi: 10.1016/j.envsoft.2013.12.019 – ident: 41_CR21 doi: 10.1109/ICECC.2011.6066743 – ident: 41_CR22 doi: 10.1109/BigData.2013.6691559 – ident: 41_CR29 – volume: 79 start-page: 1 issue: 1 year: 2014 ident: 41_CR3 publication-title: GeoJournal doi: 10.1007/s10708-013-9516-8 |
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