Urban Computing: Recent Developments and Analytics Techniques in Big Data

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : G. Muneeswari, R. Surendiran, J. Jeneetha Jebanazer, P. Josephin Shermila, E. Anna Devi, A. Jeyam
DOI : 10.14445/22315381/IJETT-V70I7P217

How to Cite?

G. Muneeswari, R. Surendiran, J. Jeneetha Jebanazer, P. Josephin Shermila, E. Anna Devi and A. Jeyam, "Urban Computing: Recent Developments and Analytics Techniques in Big Data" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 158-168, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P217

Urban computing takes strong computational procedures to tolerate such urban tasks as pollution, energy consumption, and traffic crowding. Nowadays, varied information in an urban context delivers new chances for constructing a better activity over urban computing. Though attributable to dissimilarity, extreme difficulty, and massive volumes, evaluating them is not a simple mission that frequently wants the integration of human observation into the systematic methodology, activating a wide use of Bigdata. This analysis encompasses a tendency to summarize used information varieties in urban Bigdata analytics, therefore intricate on present methods for time, locations, and various belongings of urban information. Moreover, deliberate; however, big data is combined with automatic analytical strategies. Big data analytics is that oftencomplex methodology of testing huge and varied information sets or massive data to uncover data at the side of secreted designs, unidentified parallels, arcade tendencies, and purchaser preferences that will facilitate organizations produce wise business decisions. Big data analytics would direct beneficial data for several administrations. Big data study, though, is still in its early stages. Its attention is rather undistinguishable, and interrelated studies are not well incorporated. The analysis work has provided solutions supported processing techniques, which could meet various challenges in urban turning out and management. Here, outlooks the end of the day of urban big data analytics and achieves the review with probable analysis objectives.

Big data analytics, Urban computing, Urban data, Urban data analytics, Urbanization.

[1] S. S. Siddiqui, and D. Gupta, “Big Data Process Analytics: A Survey,” Int J Emerg Res Manag Technol, vol.3, no.7, pp.117-23, 2014.
[2] H. Hu, Y. Wen, T. S. Chua, and X. Li, "Toward Scalable Systems for Big Data Analytics: A Technology Tutorial,” Ieee Access, vol. 2, pp 652-687, 2014.
[3] R. Surendiran, "Development of Multi Criteria Recommender System," Ssrg International Journal of Economics and Management Studies, vol. 4, no. 1, pp.31-35, 2017. Https://Doi.Org/10.14445/23939125/Ijems-V4i1p106
[4] N. Raden, and H. Brains, “Big Data Analytics Architecture,” Hired Brains Inc, [Online]. Available: Www.Teradata.Com/BigDataanalytics, 2012.
[5] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. Hung Byers, Big Data: the Next Frontier for Innovation, Competition, and Productivity, Mckinsey Global Institute, [Online]. Available: Http://Hdl.Handle.Net/2324/3144682, (Accessed: 2011)
[6] R. Surendiran, "Secure Software Framework for Process Improvement," Ssrg International Journal of Computer Science and Engineering, vol. 3, no. 12, pp.19-25, 2016. Https://Doi.Org/10.14445/23488387/Ijcse-V3i12p105.
[7] K. Duraisamy, R. Surendiran, “Impacts of E-Content,” International Journal of Computer Trends and Technology, vol. 1, no. 1, pp.1- 4, 2011. Https://Doi.Org/10.14445/22312803/Ijctt-V1i1p8.
[8] S. Ji, Y. Zheng, and T. Li, “Urban Sensing Based on Human Mobility,” In Proc. Acm Inter. Joint Conf. Pervasive and Ubiq. Comp, pp. 1040-1051, 2016.
[9] Y. Zheng, “Trajectory Data Mining: an Overview, “ Acm Trans. Intellig. System and Techn, vol. 6, no.3, pp.1-41, 2015.
[10] J. Bao, R. Li, X. Yi, and Y. Zheng, “Managing Massive Trajectories on the Cloud,” In Proc. 24th Acm Sigspatial Inter. Conf. Adv. Geographic Inform. Systems, pp. 1-10, 2016.
[11] Y. Zheng, “Methodologies for Cross-Domain Data Fusion: an Overview,” Ieee Trans Big Data, vol.1, no.1, pp.16-34, 2015.
[12] J. Zhang, Y. Zheng, and D. Qi, “Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction,” In Proc. ThirtyFirst Aaai Conf. Artificial Intelligence, 2017.
[13] D. Liu, D. Weng, Y. Li, J. Bao, Y. Zheng, H. Qu, and Y. Wu, “Smartadp: Visual Analytics of Large-Scale Taxi Trajectories for Selecting Billboard Locations,” Ieee Trans. Visualiz. Comp. Graph, vol.23, no.1, pp. 1-10, 2017.
[14] Y. Zheng, F. Liu, and H. P. Hsieh, “U-Air: When Urban Air Quality Inference Meets Big Data,” In Proc. 19th Acm Sigkdd Inter. Conf. Knowledge Discov. Data Mining, pp.1436-1444, 2013.
[15] Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li, “Forecasting Fine-Grained Air Quality Based on Big Data,” In Proc. 21th Acm Sigkdd Inter. Conf. Knowledge Discovery and Data Min, pp. 2267-2276, 2015.
[16] J. Gantz, and D. Reinsel, “the Digital Universe In 2020: Big Data, Bigger Digital Shadows, and Biggest Growth In the Far East,” Idc Iview: Idc Analyze the Future, vol.2007, pp. 1-16, 2012.
[17] W. Fan, A. Bifet, Mining Big Data: Current Status, and Forecast to the Future”, Acm Sigkdd Explorations News, vol.14, no.2 pp. 1-5, 2013.
[18] M. Cooper, P. Mell, [Online]. Available: Http://Csrc.Nist.Gov/Groups/Sma/Forum/Docu Ments/June2012presentations/F%Csm_June20 12_Cooper_Mell.Pdf (Accessed: 2012)
[19] Sumitha Manoj, R. Surendiran, "Investigation of Duty Cycle Distortion In Clock Channels with Infinisim Clockedge Technology," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp.457-464, 2022. Https://Doi.Org/10.14445/22315381/Ijett-V70i4p238.
[20] R. Surendiran, K.Alagarsamy, “Skin Detection Based Cryptography In Steganography (Sdbcs),” International Journal of Computer Science and Information Technologies, vol. 1, no. 4, pp.221-225, 2010.