Big Data Architecture Framework for Data Analysis and Processing in Ecosystem

Authors

  • K. Chanthirasekaran Professor, Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai. https://orcid.org/0000-0002-1856-028X
  • S. Yogeeswaran Assistant professor, Department of ECE, P.T.Lee Chengalvaraya Naicker College of Engineering and Technology, Oovery, Kanchipuram-631 502, Tamilnadu, India
  • Rafikullah Deraman Project and Facilities Management Research Group, Faculty of Civil Engineering and Built Environment, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia.
  • T. Jackulin Associate Professor, Panimalar Engineering College, Chennai, India.
  • S. V. Manikanthan Director, Melange Academic Research Associates, Puducherry, India

Keywords:

Unstructured data, Big Data Architecture Framework (BDAF), Ecosystem, bid data technology

Abstract

Big Data is a well-known buzzword in industry and academics, but there is still much conceptual uncertainty surrounding it, making it difficult to understand what it really means. The phrase is used to denote a wide range of concepts, from technology's ability to receive, repair, and use information to the cultural transformation that is extensively influencing both industries and societies, both of which are drowning in information. Research has gone in a lot of different areas because there is no official definition. This article explores the nature of big data, which can come from various fields of science, business, and social activity. It also suggests an improved big data definition that contains the following elements: Aspects of large data include facilities, safety, data infrastructure, data concepts, and data architectures. This article talks about the paradigm change in big data operations from traditional host- or service-oriented design to data-centric architecture. The Big Data Architecture Framework (BDAF), which is proposed to handle every area of the Big Data Ecosystem, is made up of the following components. As a result, the analysis of big data is currently the subject of study and development. Examining the possible effects of big data challenges, unanswered research problems, and related tools is the primary objective of this work. Thus, this paper provides a paradigm for investigating big data at different stages. Academics now have novel possibilities to develop the solution as a result of the difficulties and open research problems.

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References

Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In 2013 international conference on collaboration technologies and systems (CTS) (pp. 42-47). IEEE.

Arfat, Y., Usman, S., Mehmood, R., & Katib, I. (2020). Big data for smart infrastructure design: Opportunities and challenges. Smart Infrastructure and Applications: Foundations for Smarter Cities and Societies, 491-518.

Orenga-Roglá, S., & Chalmeta, R. (2018). Framework for implementing a big data ecosystem in organizations. Communications of the ACM, 62(1), 58-65.

Cui, Y., Kara, S., & Chan, K. C. (2020). Manufacturing big data ecosystem: A systematic literature review. Robotics and computer-integrated Manufacturing, 62, 101861.

Khalifa, S., Elshater, Y., Sundaravarathan, K., Bhat, A., Martin, P., Imam, F., ... & Statchuk, C. (2016). The six pillars for building big data analytics ecosystems. ACM Computing Surveys (CSUR), 49(2), 1-36.

Anwar, M. J., Gill, A. Q., Hussain, F. K., & Imran, M. (2021). Secure big data ecosystem architecture: challenges and solutions. EURASIP Journal on Wireless Communications and Networking, 2021(1), 130.

Allam, S. Research on the Secure Medical Big Data Ecosystem Based on Hadoop. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320(2882), 815-819.

Benhlima, L. (2018). Big data management for healthcare systems: architecture, requirements, and implementation. Advances in bioinformatics, 2018.

Qin, P., Li, W., & Ding, K. (2022). A big data security architecture based on blockchain and trusted data cloud center. Wireless Communications and Mobile Computing, 2022.

Farley, S. S., Dawson, A., Goring, S. J., & Williams, J. W. (2018). Situating ecology as a big-data science: Current advances, challenges, and solutions. BioScience, 68(8), 563-576.

Kumar, S., & Singh, M. (2018). Big data analytics for healthcare industry: impact, applications, and tools. Big data mining and analytics, 2(1), 48-57.

Demchenko, Y., De Laat, C., & Membrey, P. (2014, May). Defining architecture components of the Big Data Ecosystem. In 2014 International conference on collaboration technologies and systems (CTS) (pp. 104-112). IEEE.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of business research, 70, 263-286.

L’heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. (2017). Machine learning with big data: Challenges and approaches. Ieee Access, 5, 7776-7797.

Begoli, E., & Horey, J. (2012, August). Design principles for effective knowledge discovery from big data. In 2012 Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture (pp. 215-218). IEEE.

Begoli, Edmon, and James Horey. "Design principles for effective knowledge discovery from big data." In 2012 Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture, pp. 215-218. IEEE, 2012.

Khare, S. ., & Badholia, A. . (2023). BLA2C2: Design of a Novel Blockchain-based Light-Weight Authentication & Access Control Layer for Cloud Deployments. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 283–294. https://doi.org/10.17762/ijritcc.v11i3.6359

Kamau, J., Ben-David, Y., Santos, M., Joo-young, L., & Tanaka, A. Predictive Analytics for Customer Churn in the Telecom Industry. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/130

Chaudhury, S., Dhabliya, D., Madan, S., Chakrabarti, S. Blockchain technology: A global provider of digital technology and services (2023) Building Secure Business Models Through Blockchain Technology: Tactics, Methods, Limitations, and Performance, pp. 168-193.

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Published

27.10.2023

How to Cite

Chanthirasekaran, K. ., Yogeeswaran , S. ., Deraman, R. ., Jackulin, T. ., & Manikanthan, S. V. . (2023). Big Data Architecture Framework for Data Analysis and Processing in Ecosystem. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 435–443. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3643

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

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