APPROACH FOR URBAN TERRITORY PLANNING BASED BIG DATA

  • LOUARDI BRADJI Department of MI, University of Tebessa, Algeria
  • KHAOULA TABET Department of MI, University of Tebessa, Algeria
  • MOHAMED RIDDA LAOUAR Department of MI, University of Tebessa, Algeria

Résumé

Over the last few years, urban data has become more complex for the reason that large amount of data are being available lately, along with the rapid change of technologies and mobile applications and new problems have discovered. Therefore, urban territory planning organizations have believed that urban data analytics tools are really important subject in order to manage a large amount of complex data, which can lead to improve urban territory planning and help urban practice to reach a high level of efficiency and work flow accuracy, if these data analytics tools applied correctly, but the questions are how urban organizations are applying these tools today, and how to think about it‟s future use? This paper gives a response to this question by proposing an approach that combines big data technologies such as NoSQL systems ,Hadoop and MapReduce. We have used current technologies to implement and validate the proposed approach which offers the ability to handle large data to achieve better decision in urban territory planning domain.

Références

[1] F. Martin-Sanchez, K. Verspoor, “Big Data in Medicine Is Driving Big”, IMIA Yearbook of Medical Informatics 2014:14-20.
[2] Web site wiseGEEK.com paper written by Niki Foster 02 Jan 2015.
[3] B. Di Martino, R. Aversa, G. Cretella, A. Esposito, J. Kołodziej, “Big data (lost) in the cloud”, Int. J. Big Data Intelligence, Vol. 1, Nos. 1/2, 2014 .
[4] Bo Li, boli“Survey of Recent Research Progress and Issues in Big Data”, CSE. Wustlr.edu , 2013.
[5] Fabricio F. Costa, “Big data in biomedicine”, Drug Discovery Today _ Volume 00, Number 00 _ November 2013.
[6] Pusala M. K., Salehi M.A., Katukuri J. R., Xie Y., Raghavan V. V., “Massive Data Analysis: Tasks, Tools, Applications and Challenges” . Chapter In book: Big Data Analytics, Publisher: Springer, January 2016.
[7] T. Davenport and J. Dyché, “Big Data in Big Companies”, SAS Institute Inc. May 2013.
[8] Hassanien A., · Azar A. T., S. Vaclav, K. Janusz, H. Abawajy “Big Data in Complex Systems: Challenges and Opportunities”, Studies in Big Data, Volume 9, Series editor: Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland, springer, 2015.
[9] Laura B. Madsen, “Data-Driven Healthcare: How Analytics and BI are transforming the industry”, Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2014.
[10] S. Landset, Taghi M. Khoshgoftaar, T. Hasanin, “A survey of open source tools for machine learning with big data in the Hadoop ecosystem”, Journal of Big Data (2015) 2:24.
[11] J. Trevor, William A. Young, Gary R. Weckman, "Defining, Understanding, and Addressing Big Data." IJBAN 3.2 (2016): 1-32. Web. 16 Jun. 2016.
[12] B. Shankar, P. Mishra, S. Dehuri, E. Kim, G. Wang, “Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing”, Studies in Big Data, Volume 17, Series editor: Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland, 2016.
[13] S. P. M. Claps , “Bigger Data for Better Healthcare”, Sep 2013, IDC Health Insights.
[14] C. Lam, “Hadoop in Action”, Manning Publications Co., Greenwich, CT, USA, 2010.
[15] A. Shinnar, D. Cunningham, V. Saraswat, B. Herta. “M3r: Increased performance for in-memory hadoop jobs.”, Proceedings of VLDB Endowment, 5(12):1736–1747, Aug 2012.
[16] T. Gunarathne, T. Wu, J. Qiu, G. Fox, “Cloud Computing Paradigms for Pleasingly Parallel Biomedical Applications”, HPDC‟10, June 20–25, 2010, Chicago, Illinois, USA.
[17] V. Srinivas Agneeswaran, “Big-Data – Theoretical, Engineering and Analytics Perspective”, First International Conference, BDA 2012, New Delhi, India, December 24-26, 2012, Proceedings, LNCS 7678, pp. 8–15.
[18] SM. Freire, D. Teodoro, F. Wei-Kleiner, E. Sundvall, D. Karlsson, P. Lambrix, “Comparing the Performance of NoSQL Approaches for Managing Archetype-Based Electronic Health Record Data.”, PLoS ONE 11(3): e0150069. doi:10.1371/ journal.pone.0150069 Editor: Kim W Carter, University of Western Australia, 2016.
[19] L. Rocha, F. Vale, E. Cirilo, D. Barbosa, F. Mourao, “Framework for Migrating Relational Datasets to NoSQL”, Procedia Computer Science Volume 51, 2015, Pages 2593–2602, ICCS 2015.
[20] C. J. Tauro, B. R. Patil, K. R. Prashanth, “A Comparative Analysis of Different NoSQL Databases on Data Model, Query Model and Replication Model.”, In Proceedings of the International Conference on ERCICA.
[21] V. N. Gudivada, D. Rao , V. Raghavan, “Data Management Issues in Big Data Applications”, ALLDATA 2015 : The First International Conference on Big Data, Small Data, Linked Data and Open Data.
[22] F. Carini, “Mobility Analysis for Smart Cities: Territorial Intel l igence and Big Data”, 2013.
[23] T. A. M. Phan, J. K. Nurminen and M. Di Francesco, "Cloud Databases for Internet-of-Things Data," Internet of Things (iThings), IEEE International Conference on, and Green Computing and Communications, IEEE and Cyber, Physical and Social Computing, Taipei, 2014, pp. 117-124.
[24] T. Harter, D. Borthakur, S. Dong, A. Aiyer, L. Tang, “Analysis of HDFS Under HBase: A Facebook Messages Case Study”, Proceedings of the 12th USENIX Conference on File and Storage Technologies (FAST ‟14), February 17–20, 2014 • Santa Clara, CA USA.
Publiée
2017-02-05
Comment citer
BRADJI, LOUARDI; TABET, KHAOULA; LAOUAR, MOHAMED RIDDA. APPROACH FOR URBAN TERRITORY PLANNING BASED BIG DATA. Courrier du Savoir, [S.l.], v. 22, fév. 2017. ISSN 1112-3338. Disponible à l'adresse : >http://revues.univ-biskra.dz/index.php/cds/article/view/1906>. Date de consultation : 16 déc. 2017