Exploring Unconventional Sources in Big Data: A Comprehensive Data Lifecycle for Social and Economic Analysis

Authors

  • Mahadevi Somnath Namose Research Scholar, Department of Computer Science and Engineering, Sardar Patel University, Bhopal, MP, India.
  • Tryambak Hiwarkar Professor, Department of Computer Science and Engineering, Sardar Patel University, Bhopal, MP, India

Keywords:

information security, information system, security awareness, user behavior

Abstract

In the era of information proliferation, the availability of data from unconventional sources has significantly expanded the horizons of social and economic analysis. Many socio-economic indices have historically had extraordinarily high levels of volatility in some countries, particularly those in the developing world. Volatility in important economic indicators, such as commodity prices, the unemployment rate, currency exchange rates, etc., can have a detrimental effect on a nation's economic health. Organizations and academics must deal with a huge volume of unstructured and heterogeneous data in order to convert it into meaningful information. one must carefully plan and arrange the entire data analysis process while considering the unique characteristics of social and economic studies, which include a wide range of heterogeneous information sources and a tight governance policy. This paper delves into the realm of big data, focusing on its unconventional sources and proposing a comprehensive data lifecycle tailored for social and economic analyses. Traditional data sources, while valuable, often fall short in capturing the complexities of modern societies and economies. As such, this research navigates through a diverse range of unconventional sources, including social media streams, sensor data, satellite imagery, and more, to harness their potential in providing novel insights. The proposed data lifecycle serves as a strategic framework to manage the entire journey of data in the context of social and economic analysis. Encompassing data acquisition, preprocessing, storage, analysis, and interpretation, the lifecycle acknowledges the unique challenges posed by unconventional sources. Privacy concerns, data veracity, and ethical considerations are addressed to ensure robust analytical outcomes while upholding data rights and societal values. In conclusion, this research underscores the significance of exploring unconventional sources in big data for holistic social and economic analysis. By embracing a comprehensive data lifecycle, researchers, analysts, and decision-makers can navigate the intricacies of these data sources while upholding ethical standards and maximizing their utility. As we stand at the intersection of technology and human progress, this paper paves the way for harnessing the power of unconventional data to drive positive and informed change in our interconnected world.

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References

J. VAN WEZEL ET AL., “DATA LIFE CYCLE LABS, A NEW CONCEPT TO SUPPORT DATA-INTENSIVE SCIENCE,” NO. DLCL, P. 8, 2012, [ONLINE]. AVAILABLE: HTTP://ARXIV.ORG/ABS/1212.5596.

S. I. H. Shah, V. Peristeras, and I. Magnisalis, DaLiF: a data lifecycle framework for data-driven governments, vol. 8, no. 1. Springer International Publishing, 2021.

S. Manzan, “Big Data and Computational Social Science for Economic Analysis and Policy,” Handb. Comput. Soc. Sci. Policy, pp. 231–242, 2023, doi: 10.1007/978-3-031-16624-2_12.

P. R. Chandre, P. N. Mahalle, and G. R. Shinde, “Machine Learning Based Novel Approach for Intrusion Detection and Prevention System: A Tool Based Verification,” in 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN), Nov. 2018, pp. 135–140, doi: 10.1109/GCWCN.2018.8668618.

W. Raghupathi and V. Raghupathi, “Big data analytics in healthcare: Promise and potential,” Heal. Inf. Sci. Syst., vol. 2, no. 1, pp. 1–10, 2014, doi: 10.1186/2047-2501-2-3.

M. Boenig-Liptsin, A. Tanweer, and A. Edmundson, “Data Science Ethos Lifecycle: Interplay of Ethical Thinking and Data Science Practice,” J. Stat. Data Sci. Educ., vol. 30, no. 3, pp. 228–240, 2022, doi: 10.1080/26939169.2022.2089411.

V. Sebestyén, T. Czvetkó, and J. Abonyi, “The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking,” Front. Environ. Sci., vol. 9, no. March, pp. 1–26, 2021, doi: 10.3389/fenvs.2021.619092.

P. Chandre, P. Mahalle, and G. Shinde, “Intrusion prevention system using convolutional neural network for wireless sensor network,” IAES Int. J. Artif. Intell., vol. 11, no. 2, pp. 504–515, 2022, doi: 10.11591/ijai.v11.i2.pp504-515.

M. P. Libório, P. I. Ekel, and C. A. P. da Silva Martins, “Economic analysis through alternative data and big data techniques: what do they tell about Brazil?,” SN Bus. Econ., vol. 3, no. 1, pp. 1–16, 2022, doi: 10.1007/s43546-022-00387-z.

Mahadevi Somnath Namose and Dr. Tryambak Hiwarkar, “Predictive Analytics Accomplished by the Utilization of Social Big Data and Machine Learning,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 2, no. 1, pp. 828–833, 2022, doi: 10.48175/ijarsct-7570.

Mahadevi Somnath Namose and Dr. Tryambak Hiwarkar, “Predictive Analytics Executed through the Use of Social Big Data and Machine Learning: An Imperious Result,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 2, no. 3, pp. 55–64, 2022, doi: 10.48175/ijarsct-7598.

A. Ferretti et al., “Ethics review of big data research: What should stay and what should be reformed?,” BMC Med. Ethics, vol. 22, no. 1, pp. 1–13, 2021, doi: 10.1186/s12910-021-00616-4.

K. G. Abraham, R. S. Jarmin, B. C. Moyer, and M. D. Shapiro, “Big Data for Twenty-First-Century Economic Statistics,” Big Data Twenty-First-Century Econ. Stat., no. 28, 2022, doi: 10.7208/chicago/9780226801391.001.0001.

X. Li and R. Law, “Network analysis of big data research in tourism,” Tour. Manag. Perspect., vol. 33, no. November 2019, p. 100608, 2020, doi: 10.1016/j.tmp.2019.100608.

D. Blazquez and J. Domenech, “Big Data sources and methods for social and economic analyses,” Technol. Forecast. Soc. Change, vol. 130, no. March 2017, pp. 99–113, 2018, doi: 10.1016/j.techfore.2017.07.027.

R. Wang et al., “Review on mining data from multiple data sources,” Pattern Recognit. Lett., vol. 109, pp. 120–128, 2018, doi: 10.1016/j.patrec.2018.01.013.

Y. Lin, H. Wang, J. Li, and H. Gao, “Data source selection for information integration in big data era,” Inf. Sci. (Ny)., vol. 479, pp. 197–213, 2019, doi: 10.1016/j.ins.2018.11.029.

S. Ren, Y. Zhang, Y. Liu, T. Sakao, D. Huisingh, and C. M. V. B. Almeida, “A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions,” J. Clean. Prod., vol. 210, no. January 2022, pp. 1343–1365, 2019, doi: 10.1016/j.jclepro.2018.11.025.

J. Koo, G. Kang, and Y. G. Kim, “Security and privacy in big data life cycle: A survey and open challenges,” Sustain., vol. 12, no. 24, pp. 1–32, 2020, doi: 10.3390/su122410571.

P. Jain, M. Gyanchandani, and N. Khare, “Big data privacy: a technological perspective and review,” J. Big Data, vol. 3, no. 1, 2016, doi: 10.1186/s40537-016-0059-y.

A. Sinaeepourfard, X. Masip-Bruin, J. Garcia, and E. Marín-Tordera, “A Survey on Data Lifecycle Models: Discussions toward the 6Vs Challenges,” Tech. Rep., 2015.

Y. Wang, L. A. Kung, and T. A. Byrd, “Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations,” Technol. Forecast. Soc. Change, vol. 126, pp. 3–13, 2018, doi: 10.1016/j.techfore.2015.12.019.

L. Tang, J. Li, H. Du, L. Li, J. Wu, and S. Wang, “Big Data in Forecasting Research: A Literature Review,” Big Data Res., vol. 27, p. 100289, 2022, doi: 10.1016/j.bdr.2021.100289.

X. Bai et al., “Educational Big Data: Predictions, Applications and Challenges,” Big Data Res., vol. 26, p. 100270, 2021, doi: 10.1016/j.bdr.2021.100270.

V.S. Kore, B.A. Tidke and P. Chandre, "Survey of image retrieval techniques and algorithms for image-rich information networks", International Journal of Computer Applications, vol. 112, no. 6, pp. 39-42, 2015.

Funde Rahul, Chandre Pankaj "Dynamic cluster head selection to detect gray hole attack using intrusion detection system in MANETs" Proceedings of the Sixth International Conference on Computer and Communication Technology, ACM (2015), pp. 73-77.

Deshpande S, Gujarathi J, Chandre P, Nerkar P. "A comparative analysis of machine deep learning algorithms for intrusion detection in wsn." In: Security Issues and Privacy Threats in Smart Ubiquitous Computing, 2021; pp. 173–193. Springer.

Balasubramanian, S. ., Naruka, M. S. ., & Tewari, G. (2023). Denoising ECG Signal Using DWT with EAVO . International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 231–237. https://doi.org/10.17762/ijritcc.v11i3s.6184

Juhani Nieminen , Johan Bakker, Martin Mayer, Patrick Schmid, Andrea Ricci. Exploring Explainable AI in Educational Machine Learning Models. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/191

Anupong, W., Azhagumurugan, R., Sahay, K. B., Dhabliya, D., Kumar, R., & Vijendra Babu, D. (2022). Towards a high precision in AMI-based smart meters and new technologies in the smart grid. Sustainable Computing: Informatics and Systems, 35 doi:10.1016/j.suscom.2022.100690

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Published

03.09.2023

How to Cite

Namose, M. S. ., & Hiwarkar, T. . (2023). Exploring Unconventional Sources in Big Data: A Comprehensive Data Lifecycle for Social and Economic Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 809–820. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3553

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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