Abberley, L., Gould, N., Crockett, K., & Cheng, J. (2017, Sept). Modelling road congestion using ontologies for big data analytics in smart cities. In 2017 international smart cities conference (isc2) (p. 1-6). doi: 10.1109/ISC2.2017.8090795

Abu-Elkheir, M., Hassanein, H. S., & Oteafy, S. M. A. (2016, Sept). Enhancing emergency response systems through leveraging crowdsensing and heterogeneous data. In 2016 international wireless communications and mobile computing conference (iwcmc) (p. 188-193). doi: 10.1109/IWCMC.2016 .7577055

Aguilera, U., Peña, O., Belmonte, O., & López-de Ipiña, D. (2017). Citizen-centric data services for smarter cities. Future Generation Computer Systems, 76 , 234-247.

Al Nuaimi, E., Al Neyadi, H., Mohamed, N., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6 (1), 25.

Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton, New Jersey, USA: Princeton university press.

Anisetti, M., Ardagna, C., Bellandi, V., Cremonini, M., Frati, F., & Damiani, E. (2018, May). Privacy aware Big Data Analytics as a service for public health policies in smart cities. Sustainable Cities and Society, 39 , 68-77.

Antonić, A., Marjanović, M., Pripužić, K., & Žarko, I. P. (2016). A mobile crowd sensing ecosystem enabled by cupus: Cloud-based publish/subscribe middleware for the internet of things. Future Generation Computer Systems, 56 , 607-622.

Arimond, A., Kofler, C., & Shafait, F. (2010). Distributed pattern recognition in RapidMiner. In Proceedings of rapidminer community meeting and conference.

Athey, S. (2017). Impact of machine leaning on economics. The national bureau of economic research. Babar, M., & Arif, F. (2017). Smart urban planning using big data analytics to contend with the interop erability in internet of things. Future Generation Computer Systems, 77 , 65-76.

Barkham, R., Bokhari, S., & Saiz, A. (2018). Urban big data: City management and real estate markets (Tech. Rep.). GovLab.

Barns, S. (2017). Smart cities and urban data platforms: Designing interfaces for smart governance. City, Culture and Society.

Bassoo, V., Ramnarain-Seetohul, V., Hurbungs, V., Fowdur, T. P., & Beeharry, Y. (2018). Big data analytics for smart cities. In N. Dey, A. E. Hassanien, C. Bhatt, A. S. Ashour, & S. C. Satapathy (Eds.), Internet of things and big data analytics toward next-generation intelligence (pp. 359-379). Cham: Springer International Publishing. Retrieved from https://doi.org/10.1007/978-3-319-60435-0_15 doi: 10.1007/978-3-319-60435-0_15

Bawany, N. Z., & Shamsi, J. A. (2015). Smart city architecture: Vision and challenges. International Journal of Advanced Computer Science and Applications, 6 (11), 246-255.

Bekker, P. A. (1994). Alternative approximations to the distributions of instrumental variable estimators. Econometrica, 62 (3), 657-681.

Bibri, S. E. (2017). The iot for smart sustainable cities of the future: An analytical framework for sensor based big data applications for environmental sustainability. Sustainable Cities and Society.

Bibri, S. E., & Krogstie, J. (2017). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31 , 183-212.

Bilal, K., Khalid, O., Erbad, A., & Khan, S. U. (2018). Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. Computer Networks, 130 , 94-120.

Blumenstock, J. E. (2016). Fighting poverty with data. Science, 353 (6301), 753-754. Retrieved from http://science.sciencemag.org/content/353/6301/753 doi: 10.1126/science.aah5217

Bolívar, M. P. R. (2015). Smart cities: Big cities, complex governance? In Transforming city governments for successful smart cities (pp. 1-7). Springer.

Camerer, C. F. (2017). Artificial intelligence and behavioral economics. In Economics of Artificial Intelli gence.

Carli, R., Albino, V., Dotoli, M., Mummolo, G., & Savino, M. (2015). A dashboard and decision support tool for the energy governance of smart cities. In Environmental, energy and structural monitoring systems (eesms), 2015 ieee workshop on (pp. 23-28).

Chatfield, A. T., & Reddick, C. G. (2017). Customer agility and responsiveness through big data analytics for public value creation: A case study of houston 311 on-demand services. Government Information Quarterly.

Chatterjee, S., & Kar, A. K. (2015, Aug). Smart cities in developing economies: A literature review and policy insights. In 2015 international conference on advances in computing, communications and informatics (icacci) (p. 2335-2340). doi: 10.1109/ICACCI.2015.7275967 Chauhan, S., Agarwal, N., & Kar, A. K. (2016, Jun). Addressing big data challenges in smart cities:

a systematic literature review. info, 18 (4), 73-90. Retrieved from http://dx.doi.org/10.1108/info-03-2016-0012 doi: 10.1108/info-03-2016-0012

Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining for the internet

of things: literature review and challenges. International Journal of Distributed Sensor Networks,

11 (8), 431047.

Clare. (2016, July 27). Global smart energy market 2016-2020 [Online]. Retrieved 5th

May 2018 from https://www.prnewswire.com/news-releases/global-smart-energy-market

-2016-2020-300304980.html.

Cortes, C., & Vapnik, V. (1995, Sep 01). Support-vector networks. Machine Learning, 20 (3), 273-297.

Retrieved from https://doi.org/10.1023/A:1022627411411 doi: 10.1023/A:1022627411411

Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., & Verriello, R. (2015). A cyber - based dss for a low

carbon integrated waste management system in a smart city. IFAC-PapersOnLine, 48 (3), 2356 -

2361. Retrieved from http://www.sciencedirect.com/science/article/pii/S2405896315006795(15th IFAC Symposium onInformation Control Problems inManufacturing) doi: https://doi.org/

10.1016/j.ifacol.2015.06.440

Docherty, I., Marsden, G., & Anable, J. (2017). The governance of smart mobility. Transportation Research

Part A: Policy and Practice.

Druedah, J., & Munk-Nielsen, A. (2018). Higher-order income dynamics with linked regression trees. In 2nd

conference on dynamic structural models: Methodology and applications of structural dynamic models

and machine learning. Department of Economics, University of Copenhagen.

Dustdar, S., Nastic, S., & Scekic, O. (2016a). A novel vision of cyber-human smart city. In Hot topics in

web systems and technologies (hotweb), 2016 fourth ieee workshop on (pp. 42-47).

Dustdar, S., Nastic, S., & Scekic, O. (2016b, Oct). A novel vision of cyber-human smart city. In 2016

fourth ieee workshop on hot topics in web systems and technologies (hotweb) (p. 42-47). doi: 10.1109/

HotWeb.2016.16

Edmondson, V., Cerny, M., Lim, M., Gledson, B., Lockley, S., & Woodward, J. (2018). A smart sewer asset

information model to enable an ‘Internet of Things' for operational wastewater management. Au tomation in Construction, 91 , 193 - 205. Retrieved from http://www.sciencedirect.com/science/article/pii/S0926580517306945 doi: https://doi.org/10.1016/j.autcon.2018.03.003

Eirinaki, M., Dhar, S., Mathur, S., Kaley, A., Patel, A., Joshi, A., & Shah, D. (2018). A building permit

system for smart cities: A cloud-based framework. Computers, Environment and Urban Systems.

El Mendili, S., El Idrissi, Y. E. B., & Hmina, N. (2016). Benchmarking study on smart city data analytics. In

Information science and technology (cist), 2016 4th ieee international colloquium on (pp. 841-846).

enerquire. (2017, September 15). Disruptive innovation in the energy sector - definition & application to re newables [Blog post]. Retrieved from https://www.enerquire.com/blog/disruptive-innovations -in-the-energy-sector-definition-application.

Faieq, S., Saidi, R., Elghazi, H., & Rahmani, M. D. (2017). C2iot: A framework for cloud-based context aware internet of things services for smart cities. Procedia Computer Science, 110 , 151-158.

Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., & Mankodiya, K. (2018). Towards fog-driven iot ehealth: Promises and challenges of iot in medicine and healthcare. Future Genera tion Computer Systems, 78 , 659 - 676. Retrieved from http://www.sciencedirect.com/science/ article/pii/S0167739X17307677 doi: https://doi.org/10.1016/j.future.2017.04.036

Ganelin, I., Orhian, E., Sasaki, K., & York, B. (2016). Spark: Big data cluster computing in production. Wiley. Retrieved from https://books.google.co.uk/books?id=SDa7CwAAQBAJ

Gartner. (n.d.). Gartner hype cycle. Retrieved 22nd August, 2018, from https://www.gartner.com/en/ research/methodologies/gartner-hype-cycle

Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and google street view to estimate the demographic makeup of neighborhoods across the united states. Proceedings of the National Academy of Sciences, 114 (50), 13108-13113. Retrieved from http://www.pnas.org/content/114/50/13108 doi: 10.1073/pnas.1700035114

Ghaemi, Z., Alimohammadi, A., & Farnaghi, M. (2018, Apr 20). LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran. Environmental Monitoring and Assessment, 190 (5), 300. Retrieved from https://doi.org/10.1007/s10661-018-6659-6 doi: 10.1007/s10661-018-6659 -6

Gil-Garcia, J. R., Zhang, J., & Puron-Cid, G. (2016). Conceptualizing smartness in government: An integrative and multi-dimensional view. Government Information Quarterly, 33 (3), 524-534.

Gori, P., Parcu, P. L., & Stasi, M. L. (2015). Smart cities and sharing economy. Robert Schuman Centre for Advanced Studies, European University Institute, Fiesole, Italy, Paper No. RSCAS 2015/96.

Gottbehüt, C. (2016, February 1). Connecting to the smart future, smart transportation (Report). Huawei. Retrieved 6th May 2018 from http://www.ey.com/Publication/vwLUAssets/Connecting -the-cities-of-the-future/$FILE/Connecting-the-cities-of-the-future.pdf.

GSMA. (2018, February). Air quality monitoring using IoT and big data (Report). Retrieved 2nd May 2018 from https://www.gsma.com/iot/wp-content/uploads/2018/02/iot_clean_air_02_18.pdf.

H., F. J., & Vipin, K. (2014). A big data guide to understanding climate change: The case for theory-guided data science. Big Data, 2 (3), 155-163. Retrieved from https://doi.org/10.1089/big.2014.0026 (PMID: 25276499) doi: 10.1089/big.2014.0026

Habibzadeh, H., Boggio-Dandry, A., Qin, Z., Soyata, T., Kantarci, B., & Mouftah, H. T. (2018). Soft sensing in smart cities: Handling 3vs using recommender systems, machine intelligence, and data analytics. IEEE Communications Magazine, 56 (2), 78-86.

Haneem, F., Kama, N., Ali, R., & Selamat, A. (2017, 09). Applying data analytics approach in systematic literature review: Master data management case study. In The 16th international conference on intelligent software methodologies, tools, and techniques (somet_17). Kitakyushu, JAPAN. doi: 10.3233/978-1-61499-800-6-705

Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., . . . Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management, 36 (5), 748-758.

Horban, V. (2016, Aug). A multifaceted approach to smart energy city concept through using big data analytics. In 2016 ieee first international conference on data stream mining processing (dsmp) (p. 392-

396). doi: 10.1109/DSMP.2016.7583585

Hu, F., Yang, C., Schnase, J. L., Duffy, D. Q., Xu, M., Bowen, M. K., . . . Song, W. (2018, June). ClimateS-

park: An in-memory distributed computing framework for big climate data analytics. Computers & Geosciences, 115 , 154-166.

Ismail, K. A., Majid, M. A., Zain, J. M., & Bakar, N. A. A. (2016, Oct). Big data prediction framework for weather temperature based on mapreduce algorithm. In 2016 ieee conference on open systems (icos) (p. 13-17). doi: 10.1109/ICOS.2016.7881981

Iyengar, S., Lee, S., Irwin, D., Shenoy, P., & Weil, B. (2018). Watthome: A data-driven approach for energy efficiency analytics at city-scale. In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining (pp. 396-405). New York, NY, USA: ACM. Retrieved from http://doi.acm.org/10.1145/3219819.3219825 doi: 10.1145/3219819.3219825

Javed, B., Khan, Z., & McClatchey, R. (2018). An adaptable system to support provenance management for the public policy-making process in smart cities. Informatics, 5 (1). doi: 10.3390/informatics5010003

Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353 (6301), 790-794. Retrieved from http://science.sciencemag.org/content/353/6301/790 doi: 10.1126/science.aaf7894

Ji, C., Wei, Y., & Poor, H. V. (2017, July). Resilience of energy infrastructure and services: Modeling, data analytics, and metrics. Proceedings of the IEEE , 105 (7), 1354-1366. doi: 10.1109/JPROC.2017 .2698262

John Walker, S. (2014, Jan). Big data: A revolution that will transform how we live, work, and think. International Journal of Advertising, 33 (1), 181-183. Retrieved from http://dx.doi.org/10.2501/ IJA-33-1-181-183 doi: 10.2501/ija-33-1-181-183

Kamilaris, A., Gao, F., Prenafeta-Boldu, F. X., & Ali, M. I. (2016, Dec). Agri-iot: A semantic framework for internet of things-enabled smart farming applications. In 2016 ieee 3rd world forum on internet of things (wf-iot) (p. 442-447). doi: 10.1109/WF-IoT.2016.7845467

Khan, Z., Anjum, A., Soomro, K., & Tahir, M. A. (2015). Towards cloud based big data analytics for smart future cities. Journal of Cloud Computing, 4 (1), 2.

Khan, Z., Dambruch, J., Peters-Anders, J., Sackl, A., Strasser, A., Fr^hlich, P., . . . Soomro, K. (2017). Developing knowledge-based citizen participation platform to support smart city decision making: The smarticipate case study. Information, 8 (2). doi: 10.3390/info8020047 Khan, Z., Pervez, Z., & Abbasi, A. G. (2017). Towards a secure service provisioning framework in a smart city environment. Future Generation Computer Systems, 77 , 112 - 135. Retrieved from http://www .sciencedirect.com/science/article/pii/S0167739X17313687 doi: https://doi.org/10.1016/j .future.2017.06.031

Kourtit, K., & Nijkamp, P. (2018). Big data dashboards as smart decision support tools for i-cities-an experiment on stockholm. Land Use Policy, 71 , 24-35.

Kousiouris, G., Akbar, A., Sancho, J., Ta-shma, P., Psychas, A., Kyriazis, D., & Varvarigou, T. (2018). An integrated information lifecycle management framework for exploiting social network data to identify dynamic large crowd concentration events in smart cities applications. Future Generation Computer Systems, 78 , 516 - 530. Retrieved from http://www.sciencedirect.com/science/article/pii/ S0167739X17300055 doi: https://doi.org/10.1016/j.future.2017.07.026

Le, D. (2018, April). Real-time air pollution prediction model based on spatiotemporal big data. arXiv.org.

LeCun, Y., Bengio, Y., & Hinton, G. (2005). Deep learning. Nature, 521 .

Li, B., Kisacikoglu, M. C., Liu, C., Singh, N., & Erol-Kantarci, M. (2017, NOVEMBER). Big data analytics for electric vehicle integration in green smart cities. IEEE Communications Magazine, 55 (11), 19-25. doi: 10.1109/MCOM.2017.1700133

Li, F., Li, R., Zhang, Z., Dale, M., Tolley, D., & Ahokangas, P. (2018, May). Big data analytics for flexible energy sharing: Accelerating a low-carbon future. IEEE Power and Energy Magazine, 16 (3), 35-42. doi: 10.1109/MPE.2018.2796358

Linder, L., Vionnet, D., Bacher, J.-P., & Hennebert, J. (2017). Big building data - a big data platform for smart buildings. Energy Procedia, 122 , 589 - 594. Retrieved from http://www.sciencedirect.com/ science/article/pii/S1876610217329582 doi: https://doi.org/10.1016/j.egypro.2017.07.354

Liu, Z., Yan, S., Cao, J., Jin, T., Tang, J., Yang, J., & Wang, Q. (2018, Dec). A bayesian approach to residential property valuation based on built environment and house characteristics. In 2018 ieee international conference on big data (big data) (p. 1455-1464). doi: 10.1109/BigData.2018.8622422

Lu, S., Li, R. M., Tjhi, W. C., Lee, K. K., Wang, L., Li, X., & Ma, D. (2011). A framework for cloud-based large-scale data analytics and visualization: Case study on multiscale climate data. In Cloud computing technology and science (cloudcom), 2011 ieee third international conference on (pp. 618-622).

Lv, Z., Li, X., Wang, W., Zhang, B., Hu, J., & Feng, S. (2018). Government affairs service platform for smart city. Future Generation Computer Systems, 81 , 443 - 451. Retrieved from http://www .sciencedirect.com/science/article/pii/S0167739X17311391 doi: https://doi.org/10.1016/j .future.2017.08.047

Malik, K. R., Sam, Y., Hussain, M., & Abuarqoub, A. (2018). A methodology for real-time data sustainability in smart city: Towards inferencing and analytics for big-data. Sustainable Cities and Society, 39 , 548 - 556. Retrieved from http://www.sciencedirect.com/science/article/pii/S2210670717305735 doi: https://doi.org/10.1016/j.scs.2017.11.031 Malik, M., Tabone, M., Chassin, D. P., Kara, E. C., Guha, R. V., & Kiliccote, S. (2017, Oct). A common data architecture for energy data analytics. In 2017 IEEE international conference on smart grid communications (smartgridcomm) (p. 417-422). doi: 10.1109/SmartGridComm.2017.8340736

Meijer, A., & Bolívar, M. P. R. (2016). Governing the smart city: a review of the literature on smart urban governance. International Review of Administrative Sciences, 82 (2), 392-408. Retrieved from https://doi.org/10.1177/0020852314564308 doi: 10.1177/0020852314564308

MGI. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy (Tech. Rep.). McKinsey Global Institute. (Available online [Last accessed: 25th April 2018])

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018, Fourthquarter). Deep learning for iot big data and streaming analytics: A survey. IEEE Communications Surveys Tutorials, 20 (4), 2923-2960. doi: 10.1109/COMST.2018.2844341

Mohammadi, M., & Al-Fuqaha, A. I. (2018). Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. CoRR, abs/1810.04107 . Retrieved from http://arxiv.org/ abs/1810.04107

Mohammadi, M., Al-Fuqaha, A. I., & Oh, J. (2018). Path planning in support of smart mobility applications using generative adversarial networks. CoRR, abs/1804.08396 . Retrieved from http://arxiv.org/ abs/1804.08396

Mohanty, S. (2015, June 10). The four essential V's for a big data analytics platform. Re trieved 28th April 2018, from http://dataconomy.com/2015/06/the-four-essentials-vs-for-a -big-data-analytics-platform/

Molka-Danielsen, J., Engelseth, P., Olesnanikova, V., Sarafin, P., 2017, & Zalman, R. (2017). Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies. In 2017 5th international conference on enterprise systems (es) (pp. 38-45). IEEE.

Motta, G., You, L., Sacco, D., & Ma, T. (2014). City feed: A crowdsourcing system for city governance. In Service oriented system engineering (sose), 2014 ieee 8th international symposium on (pp. 439-445).

Mullainathan, S., & Spiess, J. (2017, May). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31 (2), 87-106. Retrieved from http://www.aeaweb.org/articles?id=10 .1257/jep.31.2.87 doi: 10.1257/jep.31.2.87

Nguyen, K., Le, T., Nguyen, T. D., Phung, D., & Webb, G. I. (2018). Robust bayesian kernel machine via stein variational gradient descent for big data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18 . Retrieved from http://dx.doi.org/ 10.1145/3219819.3220015 doi: 10.1145/3219819.3220015

Niu, L., Tang, Q., Veeraraghavan, A., & Sabharwal, A. (2018, June). Learning from noisy web data with category-level supervision. In 2018 ieee/cvf conference on computer vision and pattern recognition (p. 7689-7698). doi: 10.1109/CVPR.2018.00802 Nyman, R., & Ormerod, P. (2016). Predicting economic recessions using machine learning algorithms. Cornell University Library (Working Paper).

Nyman, R., Ormerod, P., Smith, R., & Tuckett, D. (2014, April). Big data and economic forecasting: A top down approach using directed algorithmic text analysis. In Ecb workshop on big data for forecasting and statistics. Frankfurt.

Osman, A. M. S., Elragal, A., & Bergvall-Kåreborn, B. (2017). Big data analytics and smart cities: A loose or tight couple? In International conference on connected smart cities 2017 (csc 2017), lisbon, 20-22 july 2017.

Pan, Y., Tian, Y., Liu, X., Gu, D., & Hua, G. (2016a). Urban big data and the development of city intelligence. Engineering, 2 (2), 171-178.

Pan, Y., Tian, Y., Liu, X., Gu, D., & Hua, G. (2016b). Urban big data and the development of city intelligence. Engineering, 2 (2), 171 - 178. Retrieved from http://www.sciencedirect.com/science/ article/pii/S2095809916309456 doi: https://doi.org/10.1016/J.ENG.2016.02.003

Panetta, K. (2018, August). Top 5 trends emerge in the Gartner hype cycle for emerging technologies, 2018. Retrieved 9th April 2019, from https://www.gartner.com/smarterwithgartner/5-trends-emerge -in-gartner-hype-cycle-for-emerging-technologies-2018/

Pei, F.-Q., Li, D.-B., & Tong, Y.-F. (2018, May). Double-layered big data analytics architecture for solar cells series welding machine. Computers in Industry, 97 , 17-23.

Pérez-González, D., & Díaz-Díaz, R. (2015, feb). Public services provided with ICT in the smart city environment: The case of Spanish cities. Journal of Universal Computer Science, 21 (2), 248-267. (http://www.jucs.org/jucs_21_2/public_services_provided_with)Peters-Anders, J., Khan, Z., Loibl, W., Augustin, H., & Breinbauer, A. (2017). Dynamic, interactive and visual analysis of population distribution and mobility dynamics in an urban environment using the mobility explorer framework. Information, 8 (2). doi: 10.3390/info8020056

Pettit, C., Bakelmun, A., Lieske, S. N., Glackin, S., Hargroves, K. C., Thomson, G., . . . Newman, P. (2018). Planning support systems for smart cities. City, Culture and Society, 12 , 13 - 24. Re trieved from http://www.sciencedirect.com/science/article/pii/S1877916617302175 (Inno vation and identity in next generation smart cities) doi: https://doi.org/10.1016/j.ccs.2017.10.002

Preda, S., Oprea, S.-V., Bâra, A., & Belciu (Velicanu), A. (2018). Pv forecasting using support vector machine learning in a big data analytics context. Symmetry, 10 (12). Retrieved from http://www.mdpi.com/ 2073-8994/10/12/748 doi: 10.3390/sym10120748

PWC. (n.d.). Data driven cities (Tech. Rep.). Retrieved 26th April 2018 from https://www.pwc.ru/en/ government-and-public-sector/assets/ddc_eng.pdf.

Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016a). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101 , 63 - 80. Re trieved from http://www.sciencedirect.com/science/article/pii/S1389128616000086 (Industrial Technologies and Applications for the Internet of Things) doi: https://doi.org/10.1016/ j.comnet.2015.12.023

Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016b). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101 , 63-80.

Rathore, M. M., Paul, A., Hong, W.-H., Seo, H., Awan, I., & Saeed, S. (2017). Exploiting iot and big data analytics: Defining smart digital city using real-time urban data. Sustainable Cities and Society.

Rew, R., & Davis, G. (1990). Netcdf: an interface for scientific data access. IEEE computer graphics and applications, 10 (4), 76-82.

Rizwan, P., Suresh, K., & Babu, M. R. (2016, Oct). Real-time smart traffic management system for smart cities by using internet of things and big data. In 2016 international conference on emerging technological trends (icett) (p. 1-7). doi: 10.1109/ICETT.2016.7873660

Rouse, M. (2017, March). Big data analytics. Retrieved 28th April 2018, from https:// searchbusinessanalytics.techtarget.com/definition/big-data-analytics

Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management.

Sharif, A., Li, J., Khalil, M., Kumar, R., Sharif, M. I., & Sharif, A. (2017, Dec). Internet of things — smart traffic management system for smart cities using big data analytics. In 2017 14th interna tional computer conference on wavelet active media technology and information processing (iccwamtip) (p. 281-284). doi: 10.1109/ICCWAMTIP.2017.8301496

Shukla, S. N., & Champaneria, T. A. (2017, Feb). Survey of various data collection ways for smart trans portation domain of smart city. In 2017 international conference on i-smac (iot in social, mobile, analytics and cloud) (i-smac) (p. 681-685). doi: 10.1109/I-SMAC.2017.8058265

Silva, B. N., Khan, M., & Han, K. (2017). Integration of big data analytics embedded smart city architec ture with restful web of things for efficient service provision and energy management. Future Gener ation Computer Systems. Retrieved from http://www.sciencedirect.com/science/article/pii/ S0167739X17305174 doi: https://doi.org/10.1016/j.future.2017.06.024

Silva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38 , 697-713.

Simmhan, Y. (2018). Big data and fog computing. In S. Sakr & A. Zomaya (Eds.), Encyclopedia of big data technologies (pp. 1-10). Cham: Springer International Publishing. Retrieved from https:// doi.org/10.1007/978-3-319-63962-8_41-1 doi: 10.1007/978-3-319-63962-8_41-1

Singh, D., & Mohan, C. K. (2018, Dec). Projection-svm: Distributed kernel support vector machine for big data using subspace partitioning. In 2018 ieee international conference on big data (big data) (p. 74-83). doi: 10.1109/BigData.2018.8622478

Stephenson, D. (2013, January 18). 7 big data techniques that create business value. Retrieved 28th April 2018, from https://www.firmex.com/thedealroom/7-big-data-techniques-that-create -business-value/

Sun, Y., Song, H., Jara, A. J., & Bie, R. (2016). Internet of things and big data analytics for smart and connected communities. IEEE Access, 4 , 766-773.

Susmitha, K., & Jayaprada, S. (2017, Aug). Smart cities using big data analytics. International Research Journal of Engineering and Technology (IRJET), 4 (8), 1615-1620.

Tahat, A., Aburub, R., Al-Zyoude, A., & Talhi, C. (2018). A smart city environmental monitoring network and analysis relying on big data techniques. Proceedings of the 2018 International Conference on Software Engineering and Information Management - ICSIM2018 . Retrieved from http://dx.doi .org/10.1145/3178461.3178464 doi: 10.1145/3178461.3178464

Tan, K. H., Ji, G., Lim, C. P., & Tseng, M.-L. (2017). Using big data to make better decisions in the digital economy. International Journal of Production Research, 55 (17), 4998-5000. doi: 10.1080/ 00207543.2017.1331051

Terroso-Saenz, F., Gonzalez-Vidal, A., Cuenca-Jara, J., & Skarmeta, A. F. (2017, Oct). An open architecture for iot data analytics in smart cities. In 2017 global wireless summit (gws) (p. 235-239). doi: 10.1109/ GWS.2017.8300468

TRANSFORM Project Consortium. (2015, March 3). Work package1: ‘becoming a smart energy city, state of the art and ambition' (Tech. Rep.). Retrieved from http://urbantransform.eu/wp-content/ uploads/sites/2/2015/07/WP1-revised-final-report.march-2015.pdf.

Treadwell, J. (2017, July 11). Disruptive trends in transport - smart cities, citizens and intelligent con nections. Presentation at the 8th World Bank - Korea Transport Institute Joint Workshop, Wash ington DC, USA. (Available online: http://pubdocs.worldbank.org/en/986981501515858559/ WB-Jane-Treadwell-Disruptive-Trends-in-Transport.pdf)

van Dijk, A., & Teuben, H. (2015). Smart cities: How rapid advances in technology are reshaping our economy and society (Tech. Rep.). Deloitte. (Available online: https://www2.deloitte.com/content/dam/ Deloitte/tr/Documents/public-sector/deloitte-nl-ps-smart-cities-report.pdf [Last ac cess: 25th April 2018])

Vinod Kumar, T. M. (2015). E-governance for smart cities. In T. M. Vinod Kumar (Ed.), E-governance for smart cities (pp. 1-43). Singapore: Springer Singapore. Retrieved from https://doi.org/10.1007/ 978-981-287-287-6_1 doi: 10.1007/978-981-287-287-6_1

Wang, S., & Eick, C. F. (2017, September). A data mining framework for environmental and geo-spatial data analysis. International Journal of Data Science and Analytics, 5 (2-3), 83-98.

Wedgwood, K., & Howard, R. (2015, November). Big data and analytics in travel and transporta tion (Report). IBM. Retrieved 6th May 2018 from http://www-07.ibm.com/au/pdf/Travel _Transportation.pdf.

Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015). A literature survey on smart cities. Science China Information Sciences, 58 (10), 1-18. Zaman, H. B., Ahmad, A., Hamid, N. A., Sin, A. K., Hussain, A., Hannan, M. A., & Saad, H. M. (2017, Oct). Integrated smart neighborhood framework and application to sustain an innovative digital economy in the 4ir and big data era. In 2017 3rd international conference on science in information technology (icsitech) (p. 7-12). doi: 10.1109/ICSITech.2017.8257077

Zaouali, K., Rekik, R., & Bouallegue, R. (2018, June). Deep learning forecasting based on auto-lstm model for home solar power systems. In 2018 ieee 20th international conference on high per formance computing and communications; ieee 16th international conference on smart city; ieee 4th international conference on data science and systems (hpcc/smartcity/dss) (p. 235-242). doi: 10.1109/HPCC/SmartCity/DSS.2018.00062

Zhang, Q., Zhang, X., Zhang, Q., Shi, W., & Zhong, H. (2016). Firework: Big data sharing and processing in collaborative edge environment. In Hot topics in web systems and technologies (hotweb), 2016 fourth ieee workshop on (pp. 20-25).

Zheng, Y., Liu, F., & Hsieh, H.-P. (2013). U-air. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13 . Retrieved from http://dx.doi.org/ 10.1145/2487575.2488188 doi: 10.1145/2487575.2488188

Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., & Li, T. (2015). Forecasting fine-grained air quality based on big data. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Dis covery and Data Mining - KDD '15 . Retrieved from http://dx.doi.org/10.1145/2783258.2788573 doi: 10.1145/2783258.2788573

Zhu, D., Cai, C., Yang, T., & Zhou, X. (2018). A machine learning approach for air quality prediction: Model regularization and optimization. Big Data and Cognitive Computing, 2 (1). Retrieved from http://www.mdpi.com/2504-2289/2/1/5 doi: 10.3390/bdcc2010005

Zotano, M. A. G., & Bersini, H. (2017). A data-driven approach to assess the potential of smart cities: the case of open data for brussels capital region. Energy Procedia, 111 , 750-758.