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Internet of Things Based Wireless Body Area Network in Healthcare Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Big Data Analytics for Smart Cities

Authors : V. Bassoo, V. Ramnarain-Seetohul, V. Hurbungs, T. P. Fowdur, Y. Beeharry

Published in: Internet of Things and Big Data Analytics Toward Next-Generation Intelligence

Publisher: Springer International Publishing

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Abstract

The main objectives of smart cities are to improve the well being of its citizens and promote economic development while maintaining sustainability. Smart cities can enhance several services including healthcare, education, transportation and agriculture among others. Smart cities are based on the ICT framework including the Internet of Things (IoT) technology. These technologies create voluminous amount of heterogeneous data, which is commonly referred to as big data. However these data are meaningless on their own. New processes need to be developed to interpret the huge amount of data gathered and one solution is the application of big data analytics techniques. Big data can be mined and modelled through the analytics techniques to get better insight and to enhance smart cities functionalities. In this chapter, four state-of-the-art big data analytics techniques are presented. Applications of big data analytics to five sectors of smart cities are discussed and finally an overview of the security challenges for big data and analytics for smart cities is elaborated.

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previous chapter Internet of Things Shaping Smart Cities: A Survey
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Literature
1.
Dohler, M., Vilajosana, I., Vilajosana, X., & LLosa, J. (2011). Smart cities: An action plan. In Proceedings of Barcelona smart cities congress 2011. Barcelona.
2.
Pretz, Kathy. (2014). An urban reality: Smart cities. The Institute, 38(2), 10.
3.
4.
Deloitte. (2015). Smart cities big data. Deloitte.
5.
Datameer. (2016). Big data analytics and the internet of things.
6.
Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. Framingham, MA: EMC Corporation.
7.
Najafabadi, M. M., et al. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1.CrossRef
8.
Datameer Inc. (2013). The guide to big data analytics. In Datameer. New York: Datameer.
9.
10.
Ernst & Young. (2014). Big data changing the way business compete and operate. Ernst & Young.
11.
12.
13.
14.
MacGillivray, C., Turner, Vernon, Lamy, Lionel, Prouty, Kevin, & Segal, Rebecca. (2016). IDC future scape: Worldwide internet of things 2017 predictions. Framingham, MA: International Data Corporation.
15.
Stracke, N., Grella, M., Chiari, B., Bechle, K., & Schmid, S. (2015). Semantic technology: Intelligent solutions for big data challenges. Germany: Advanced Analytics & EXOP GmbH Constance.
16.
Saif, H., He, Y., & Alani, H. (2012). Semantic sentiment analysis of twitter. In International semantic web conference (pp. 508–524). Berlin.
17.
18.
19.
Gandomi, A., & Haider, M. (2014). Beyond the hype: Big data concepts, methods and analytics. International Journal of Information Management, 35, 137–144.CrossRef
20.
Intel IT Centre. (2013). Predictive analytics 101: Next-generation big data intelligence.
21.
22.
Parliamentary Office Science and Technology. (2014). Big and open data in transport. In Parliamentary office science and technology. London.
23.
Jagadish, H. V., et al. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94.CrossRef
24.
Sager Weinstein, L. (2015). Innovations in London’s transport: Big data for a better customer experience. London: Transport for London.
25.
Sager Weinstein, L. (2016). How TfL uses ‘big data’ to plan transport services. Euro Transport, 3.
26.
27.
28.
29.
International Transport Forum. (2015). Big data and transport understanding and assessing options. OECD.
30.
31.
32.
33.
34.
35.
36.
37.
Campbell, B. M., Thornton, P., Zougmoré, R., van Asten, P., & Lipper, L. (2014). Sustainable intensification: What is its role in climate smart agriculture? Current Opinion in Environmental Sustainability, 8, 39–43.CrossRef
38.
Alves, G. M., & Cruvinel, P. E. (2016). Big data environment for agricultural soil analysis from CT digital images. In Tenth international conference on semantic computing (ICSC) (pp. 429–431). Laguna Hills, CA.
39.
40.
41.
42.
Hippocrates. (2014). Orthopedic service line optimization. How to use big data for your value-based purchasing.
43.
44.
45.
46.
47.
48.
Poland, Michael, Nugent, Chris, Wang, Hui, & Chen, Liming. (2009). Smart home research: Projects and issues. International Journal of Ambient Computing and Intelligence (IJACI), 14(1), 14.
49.
Najjar, M., Courtemanche, F., Hamam, H., Dion, A., & Bauchet, J. (2009). Intelligent recognition of activities of daily living for assisting memory and/or cognitively impaired elders in smart homes. International Journal of Ambient Computing and Intelligence (IJACI), 1(4), 17.
50.
van Rysewyk, S. (2013). Robot pain. International Journal of Synthetic Emotions (IJSE), 4(2), 12.
51.
Belle, A., et al. (2015). Big data analytics in healthcare. BioMed Research International.
52.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2, 3.CrossRef
53.
Fang, R., Pouyanfar, S., Yang, Y., Chen, S. C., & Iyengar, S. S. (2016). Computational health informatics in the big data age: A survey. ACM Computing Surveys, 49(1), 12.CrossRef
54.
Geert, M., Fabian, G.G., Jan, R., & Maurice, B. (2009). Machine learning techniques to examine large patient databases.
55.
56.
57.
Cristobal, R., & Sebastian, V. (2013). Data mining in education (Vol. 3). New York: Wiley.
58.
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42–57.
59.
Mayer-Schonberger, V., & Cukier, K. (2014). Learning with big data the future: An eamon dolan book/houghton mifflin harcourt.
60.
Drigas Athanasios, S., & Panagiotis, L. (2014). The use of big data in education. International Journal of Computer Science, 11(5).
61.
Office of Educational Technology U.S. (2014). Department of education, enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington D.C.
62.
Arwa, I. A. (2016). Big data for accreditation: A case study of Saudi Universities. Journal of Theoretical and Applied Information Technology, 91(1).
63.
West, D. M. (2012). Big data for education: Data mining, data analytics and web dashboards. Governance Service at Brookings.
64.
Markus, I., Deirdre, K., & Hamid, M. (2012). Big data in education assessment of the new educational standards.
65.
Batt, M., et al. (2012). Smart cities of the future. The European Physical Journal Special Topics, 481–518.
66.
Stephen, K., Armour, F., Alberto, E.J., & William, M. (2013). Big data: Issues and challenges moving forward. In 46th Hawaii international conference on system sciences, HICSS’13, pp. 995–1004.
67.
Odella, F. (2016). Technology studies and the sociological debate on monitoring of social interactions. International Journal of Ambient Computing and Intelligence (IJACI), 7(1), 26.
68.
Philip Chen, C. L., & Chun-Yang, Z. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences (in press).
69.
Duygu, S.T., Terzi, R., & Sagiroglu, S. (2015). A survey on security and privacy issues in big. In The 10th international conference for internet technology and secured transactions.
70.
Cloud Security Alliance. (2012). Top ten big data security and privacy challenges.
71.
72.
Ernst & Young. (2014). Cyber insurance, security and data integrity, part1: Insights into cyber security and risk.
73.
Rajkumar, N., Vimal, Karthick R., Nathiya, M., & Silambarasan, K. (2014). Mining association rules in big data for e-healthcare information system. Research Journal of Applied Sciences, Engineering and Technology, 8(8), 1002–1008.CrossRef
74.
Fagan, D., Caulfield, B., & Meier, R. (2013). Analyzing the behavior of smartphone service users. International Journal of Ambient Computing and Intelligence (IJACI), 5(2), 16.
75.
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, 25.CrossRef
76.
Venkata, N. I., Sailaja, A., & Srinivasa, R. R. (2014). Security issues associated with big data in cloud computing. International Journal of Network Security& its Application (IJNSA), 6(3), 45.CrossRef
77.
78.
Hashem Ibrahim, A. T., et al. (2014). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 98–115.
79.
For Health Information Technology The office of the National Coordinator. (2015). Guide to privacy and security of electronic health information. USA: Department of Health and Human Services.
80.
Patil, H.K., Seshadri, R. (2014). Big data security and privacy issues in healthcare. In IEEE international congress on big data.
81.
Fatima-Zahra, B., Lahcen, A. A. (2014). Big data security: Challenges, recommendations and solutions. In Handbook of research on security considerations in cloud computing: IGI global.
82.
Sánchez, D., Martínez, S., & Domingo-Ferrer, J. (2016). How to avoid re identification with proper anonymization comment on “Unique in the shopping mall: On the reidentifiability of credit card metadata”. Science, 351(6279), 1274.
83.
Juan, Z., Yang, X., & Appelbaum, D. (2015). Toward effective big data analysis in continuous auditing. Accounting Horizons, 29(2), 469–476.CrossRef
84.
Schneider, G., & Hammer, J. H. (2007). On the definition and policies of confidentiality, pp. 337–342.
85.
Kompelli, Swetha, & Avani, Alla. (2015). Knowledge hollowing in enormous information. International Journal of computer science and Electronics Engineering, 5(4), 88–92.
86.
Kimbahune, V., Deshpande, A., & Mahalle, P. (2017). Lightweight key management for adaptive addressing in next generation internet. International Journal of Ambient Computing and Intelligence (IJACI), 8(1), 20.
87.
Min, Chen, Shiwen, Mao, & Yunhao, Liu. (2014). Big data: A survey. Mobile Network Application, 19, 171–209.CrossRef
88.
Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652–687.CrossRef
89.
Lagoze, C. (2014). Big data, data integrity, and the fracturing of the control zone. Journal of the American Society for Information Science, 63(6), 1–40.
Metadata
Title
Big Data Analytics for Smart Cities
Authors
V. Bassoo
V. Ramnarain-Seetohul
V. Hurbungs
T. P. Fowdur
Y. Beeharry
Copyright Year
2018
Book
Internet of Things and Big Data Analytics Toward Next-Generation Intelligence

Print ISBN: 978-3-319-60434-3

Electronic ISBN: 978-3-319-60435-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-60435-0_15

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