(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-7 Issue-65 April-2020
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Paper Title : Data modeling techniques used for big data in enterprise networks
Author Name : Richard Omollo and Sabina Alago
Abstract :

The deployment and maintenance of enterprise networks form the bedrock of any organization, be it government, commercial, academic, and/or non-profit making. These networks host vast amounts of information, databases, in either temporary mode while in transit or permanent mode while stationary. The databases are managed by the information systems with appropriate functions that meet consumers’ needs. Databases hold varying data – structured, semi-structured, or unstructured. Data is increasingly becoming a vital organizational asset and therefore plays a crucial role in making organizational decisions. With growth in the internet, digital data sources have become ubiquitous. In turn, this has seen the continued growth in the volume, variety, veracity, velocity, and value of data. Big data brings with its data complexities that have an eventual impact on the data modeling techniques. This paper presents a review of big data modeling techniques with a concentration of enterprise networks. We started by appreciating big data before embarking on modeling techniques for big data.

Keywords : Data, Model, Modeling techniques, Big data, Enterprise networks, Databases.
Cite this article : Omollo R, Alago S. Data modeling techniques used for big data in enterprise networks . International Journal of Advanced Technology and Engineering Exploration. 2020; 7(65):79-92. DOI:10.19101/IJATEE.2020.762029.
References :
[1]Jyothi BS, Jyothi S. A study on big data modelling techniques. International Journal of Computer Networking, Wireless and Mobile Communications. 2015; 5(6):19-26.
[Google Scholar]
[2]Tulasi B. Significance of big data and analytics in higher education. International Journal of Computer Applications. 2013; 68(14):21-3.
[Google Scholar]
[3]M. M. Huda, M. L. Hayun and Z. Martun. Data modelling for big data. ULTIMA Infosys. 2015; 6(1):1-11.
[Google Scholar]
[4]Ale B. Risk analysis and big data. In Safety and Reliability 2016; 36(3):153-65. Taylor & Francis.
[Crossref] [Google Scholar]
[5]Baumann SU, Erber IR, Gattringer MA. Selection of risk identification instruments. ACRN Oxford Journal of Finance and Risk Perspectives. 2016; 5(2):27-41.
[Google Scholar]
[6]Chen J, Tao Y, Wang H, Chen T. Big data based fraud risk management at Alibaba. The Journal of Finance and Data Science. 2015; 1(1):1-10.
[Crossref] [Google Scholar]
[7]Logica B, Magdalena R. Using big data in the academic environment. Procedia Economics and Finance. 2015; 33:277-86.
[Crossref] [Google Scholar]
[8]Patel A, Patel J. Data modeling techniques for data warehouse. International Journal of Multidisciplinary Research. 2012; 2(2):240-6.
[Google Scholar]
[9]Chaorasiya V, Shrivastava A. A survey on big data: techniques and technologies. International Journal of Research and Development in Applied Science and Engineering. 2015; 8(1):1-4.
[Google Scholar]
[10]Ularu EG, Puican FC, Apostu A, Velicanu M. Perspectives on big data and big data analytics. Database Systems Journal. 2012; 3(4):3-14.
[Google Scholar]
[11]Zhu J, Wang A. Data modeling for big data. CA, Beijing. 2012.
[Google Scholar]
[12]Austin C, Kusumoto F. The application of Big Data in medicine: current implications and future directions. Journal of Interventional Cardiac Electrophysiology. 2016; 47(1):51-9.
[Crossref] [Google Scholar]
[13]Madden S. From databases to big data. IEEE Internet Computing. 2012; 16(3):4-6.
[Crossref] [Google Scholar]
[14]Laney D. 3D data management: controlling data volume, velocity and variety. META Group Research Note. 2001.
[Google Scholar]
[15]Saabith AS, Sundararajan E, Bakar AA. Parallel implementation of apriori algorithms on the hadoop-mapreduce platform-an evaluation of literature. Journal of Theoretical and Applied Information Technology. 2016; 85:321-51.
[Google Scholar]
[16]Hadi HJ, Shnain AH, Hadishaheed S, Ahmad AH. Big data and five v’s characteristics. In IRF international conference 2014.
[Google Scholar]
[17]Anuradha J. A brief introduction on big data 5Vs characteristics and hadoop technology. Procedia Computer Science. 2015; 48:319-24.
[Crossref] [Google Scholar]
[18]Demchenko Y, De Laat C, Membrey P. Defining architecture components of the big data ecosystem. In international conference on collaboration technologies and systems 2014 (pp. 104-12). IEEE.
[Crossref] [Google Scholar]
[19]Lněnička M, Máchová R, Komárková J, Čermáková I. Components of big data analytics for strategic management of enterprise architecture. In SMSIS: proceedings of the 12th international conference on strategic management and its support by information systems 2017. Vysoká škola báňská-Technická univerzita Ostrava.
[Google Scholar]
[20]Alexandru A, Alexandru C, Coardos D, Tudora E. Healthcare, big data and cloud computing. Management. 2016; 4:123-31.
[Google Scholar]
[21]Malik BH, Cheema SN, Iqbal I, Mahmood Y, Ali M, Mudasser A. From cloud computing to fog computing (C2F): the key technology provides services in health care big data. In international conference on material engineering and advanced manufacturing technology 2018 (pp.1-7). EDP Sciences.
[Crossref] [Google Scholar]
[22]Patgiri R, Ahmed A. Big data: the vs of the game changer paradigm. In international conference on high performance computing and communications; IEEE international conference on smart city; IEEE international conference on data science and systems 2016 (pp. 17-24). IEEE.
[Crossref] [Google Scholar]
[23]Khan N, Alsaqer M, Shah H, Badsha G, Abbasi AA, Salehian S. The 10 Vs, issues and challenges of big data. In proceedings of the international conference on big data and education 2018 (pp. 52-6).
[Crossref] [Google Scholar]
[24]Sun Z, Strang K, Li R. Big data with ten big characteristics. In proceedings of the international conference on big data research 2018 (pp. 56-61).
[Crossref] [Google Scholar]
[25]Sun Z, Wang P, Strang K. A mathematical theory of big data. IEEE Transactions on Knowledge and Data Engineering. 2017; 13(2):83-99.
[Crossref] [Google Scholar]
[26]Sun Z, Sun L, Strang K. Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems. 2018; 58(2):162-9.
[Crossref] [Google Scholar]
[27]Farooqi MM, Shah MA, Wahid A, Akhunzada A, Khan F, ul Amin N, Ali I. Big data in healthcare: a survey. In applications of intelligent technologies in healthcare 2019 (pp. 143-52). Springer, Cham.
[Crossref] [Google Scholar]
[28]https://tombreur.wordpress.com/2018/12/16/the-three-vs-of-big-data-or-four-five-seven-10-or-42/. Accessed 15 December 2019.
[29]Hashem H, Ranc D. An integrative modeling of bigdata processing. International Journal of Computer Science and Applications. 2015; 12(1):1-15.
[Google Scholar]
[30]Mišić V, Velašević D, Lazarević B. Formal specification of a data dictionary for an extended ER data model. The Computer Journal. 1992; 35(6):611-22.
[Crossref] [Google Scholar]
[31]https://www.guru99.com/data-modelling-conceptual-logical.html. Accessed 15 December 2019.
[32]Liu L. Özsu MT. Encyclopedia of database systems. New York, NY, USA: Springer; 2009.
[Google Scholar]
[33]Chakraborty S, Chaki N. A survey on the semi-structured data models. In computer information systems–analysis and technologies 2011 (pp. 257-66). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[34]El Alami A, Bahaj M. The migration of a conceptual object model com (conceptual data model CDM, unified modeling language UML class diagram...) to the object relational database ORDB. MAGNT Research Report.2018; 2(4):318-27.
[Google Scholar]
[35]Ermolayev VA, Keberle NG. Active data dictionary: a method and a tool for data model driven information system design.2000.
[Google Scholar]
[36]Ramadas S. Big data analytics: tools and approaches. ICAR-Indian Institute of Wheat and Barley Research. 2017; 1-4.
[Google Scholar]
[37]https://highlyscalable.wordpress.com/2012/03/01/nosql-data-modeling-techniques/. Accessed 20 December 2019.
[38]https://www.datastax.com/blog/2018/08/evolution-nosql. Accessed 20 December 2019.
[39]https://www.dataversity.net/a-brief-history-of-non-relational-databases/. Accessed 20 December 2019.
[40]https://dzone.com/articles/understanding-the-cap-theorem. Accessed 20 December 2019.
[41]https://www.dataversity.net/choose-right-nosql-database-application/. Accessed 20 December 2019.
[42]Pritchett D. Base: an acid alternative. Queue. 2008; 6(3):48-55.
[Google Scholar]
[43]Ribeiro A, Silva A, Da Silva AR. Data modeling and data analytics: a survey from a big data perspective. Journal of Software Engineering and Applications. 2015; 8(12):617-34.
[Crossref] [Google Scholar]
[44]Farooq H, Mahmood A, Ferzund J. Do NoSQL databases cope with current data challenges. International Journal of Computer Science and Information Security. 2017; 15(4):139-46.
[Google Scholar]
[45]https://www.dataversity.net/graph-database-vs-document-database-different-levels-of-abstraction/. Accessed 20 December 2019.
[46]Besta M, Peter E, Gerstenberger R, Fischer M, Podstawski M, Barthels C et al. Demystifying graph databases: analysis and taxonomy of data organization, system designs, and graph queries. arXiv preprint arXiv:1910.09017. 2019.
[Google Scholar]
[47]Ramachandran S. Graph database theory comparing graph and relational data models. https://www.lambdazen.com/assets/pdf/GraphDatabaseTheory.pdf. Accessed 20 December 2019.
[48]Srinivasa S. Data, storage and index models for graph databases. In graph data management: techniques and applications 2012 (pp. 47-70). IGI Global.
[Crossref] [Google Scholar]