Big Data 4.0: The Era of Big Intelligence

Authors

  • Zhaohao Sun

    Department of Business Studies, PNG University of Technology, Lae 411, Papua New Guinea

DOI:

https://doi.org/10.30564/jcsr.v6i1.6054
Received: 30 October 2023 | Revised: 8 December 2023 | Accepted: 11 December 2023 | Published Online: 4 January 2024

Abstract

Big data has had significant impacts on our lives, economies, academia and industries over the past decade. The current questions are: What is the future of big data? What era do we live in? This article addresses these questions by looking at meta as an operation and argues that we are living in the era of big intelligence through analyzing from meta (big data) to big intelligence. More specifically, this article will analyze big data from an evolutionary perspective. The article overviews data, information, knowledge, and intelligence (DIKI) and reveals their relationships. After analyzing meta as an operation, this article explores Meta (DIKE) and its relationship. It reveals 5 Bigs consisting of big data, big information, big knowledge, big intelligence and big analytics. Applying meta on 5 Bigs, this article infers that Big Data 4.0 = meta4 (big data) = big intelligence. This article analyzes how intelligent big analytics support big intelligence. The proposed approach in this research might facilitate the research and development of big data, big data analytics, business intelligence, artificial intelligence, and data science.

Keywords:

Big Data 4.0; Big analytics; Business intelligence; Artificial intelligence; Data science

References

[1] Chen, C.P., Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences. 275, 314–347. DOI: https://doi.org/10.1016/j.ins.2014.01.015

[2] 100 Data and Analytics Predictions through 2021 [Internet] [cited 2018 Aug 4]. Available from: https://www.scribd.com/document/569847512/100-data-and-analytics-predictions-through-2021

[3] 30+ Incredible Big Data Statistics (2023) [Internet] [cited 2023 May 3]. Available from: https://explodingtopics.com/blog/big-data-stats#top-big-data-stats

[4] Kumar, B., 2015. An encyclopedic overview of 'big data' analytics. International Journal of Applied Engineering Research. 10(3), 5681–5705.

[5] Big Data [Internet]. Gartner. Available from: http://www.gartner.com/it-glossary/big-data/

[6] Products in Analytics and Business Intelligence Platforms Market [Internet]. Gartner [cited 2023 Jun 12]. Available from: https://www.gartner.com/reviews/market/analytics-business-intelligence-platforms

[7] Beath, C., Becerra-Fernandez, I., Ross, J., et al., 2012. Finding value in the information explosion. MIT Sloan Management Review. 53(4), 18–20.

[8] Sun, Z., Stranieri, A., 2021. The nature of intelligent analytics. Intelligent analytics with advanced multi-industry applications. IGI-Global: Hershey. pp. 1–22.

[9] Russell, S.J., Norvig, P., 2020. Artificial intelligence: A modern approach (4th edition). Prentice Hall: Upper Saddle River.

[10] Laudon, K.C., Laudon, J.P., 2020. Management information systems: Managing the digital firm (16th edition). Pearson: Harlow, England.

[11] Liu, W., Sun, Y., 2019. Big Data 3.0—The key technologies of big data in post-hadoop era. Frontiers of Data and Domputing. 1(1), 94–104. DOI: https://doi.org/10.11871/jfdc.issn.2096.742X.2019.01.010

[12] The Evolution of Big Data Analytics Market [Internet]. 3PILLARGLOBAL [cited 2021 Aug 29]. Available from: https://www.3pillarglobal.com/insights/the-evolution-of-big-data-analytics-market/

[13] McAfee, A., Brynjolfsson, E., Davenport, T.H., et al., 2012. Big data: The management revolution. Harvard Business Review. 90(10), 60–68.

[14] The Four V's of Big Data [Internet]. IBM. Available from: http://www.ibmbigdatahub.com/infographic/four-vs-bigdata

[15] Hussein, A.A., 2020. Fifty-six big data V's characteristics and proposed strategies to overcome security and privacy challenges (BD2). Journal of Information Security. 11(4), 304–328. DOI: https://doi.org/10.4236/jis.2020.114019

[16] Sun, Z., Strang, K., Li, R. (editors), 2018. Big data with ten big characteristics. Proceedings of the 2nd International Conference on Big Data Research; 2018 Oct 27–29; Weihai, China. New York: Association for Computing Machinery. p. 56–61. DOI: https://doi.org/10.1145/3291801.3291822

[17] Tsai, C.W., Lai, C.F., Chao, H.C., et al., 2015. Big data analytics: A survey. Journal of Big Data. 2(1), 1–32. DOI: https://doi.org/10.1186/s40537-015-0030-3

[18] Sathi, A., 2013. Big data analytics: Disruptive technologies for changing the game. MC Press: Boise, ID, USA.

[19] Wang, F.Y., 2012. A big-data perspective on AI: Newton, Merton, and analytics intelligence. IEEE Intelligent Systems. 27(5), 2–4. DOI: https://doi.org/10.1109/MIS.2012.91

[20] Top 10 Big Data Challenges—A Serious Look at 10 Big Data V's [Internet] [cited 2023 Jun 12]. Available from: https://bicorner.com/2015/03/31/top-10-big-data-challenges-a-serious-look-at-10-big-data-vs/

[21] Sun, Z., Sun, L., Strang, K., 2018. Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems. 58(2), 162–169. DOI: https://doi.org/10.1080/08874417.2016.1220239

[22] Big Data: The Next Frontier for Innovation, Competition, and Productivity [Internet]. McKinsey. Available from: http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation

[23] Williams, S., 2016. Business intelligence strategy and big data analytics: A general management perspective. Morgan Kaufmann: Amsterdam.

[24] Computing Curricula 2020 [Internet]. Available from: https://users.cs.fiu.edu/~prabakar/ugc/ACM_Reports/2020_ACM-IEEE_CC_Draft.pdf

[25] Anderson, D.R., Sweeney, D.J., Williams, T.A., et al., 2011. Statistics for business and economics. Cengage Learning: Boston.

[26] Coronel, C., Morris, S., Rob, P., 2020. Database systems: Design, implementation, and management (14th edition). Course Technology, Cengage Learning: Boston.

[27] Rowley, J., 2007. The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science. 33(2), 163–180. DOI: https://doi.org/10.1177/0165551506070706

[28] From Data to Wisdom [Internet] [cited 2017 Aug 31]. Available from: http://faculty.ung.edu/kmelton/Documents/DataWisdom.pdf

[29] Sabherwal, R., Becerra-Fernandez, I., 2011. Business intelligence: Practices, technologies, and management. John Wiley & Sons, Inc.: Hoboken, NJ.

[30] Liew, A., 2013. DIKIW: Data, information, knowledge, intelligence, wisdom and their interrelationships. Business Management Dynamics. 2(10), 49–62.

[31] Wang, Y., 2015. Formal cognitive models of data, information, knowledge, and intelligence. WSEAS Transactions on Computers. 14(3), 770–781.

[32] Sun, Z., 2023. Similarity intelligence: Similarity based reasoning, computing, and analytics. Journal of Computer Science Research. 5(3), 1–14.

[33] Turban, E., Volonino, L., 2011. Information technology for management: Improving performance in the digital economy. John Wiley & Sons: Hoboken, NJ.

[34] Oxford, 2008. Oxford advanced learner's English dictionary (7th edition). Oxford University Press: Oxford.

[35] Sun, Z., Wang, P.P., 2017. Big data, analytics, and intelligence: An editorial perspective. Journal of New Mathematics and Natural Computation. 13(2), 75–81. DOI: https://doi.org/10.1142/S179300571702001X

[36] Sun, Z., 2022. Problem-based computing and analytics. International Journal of Future Computer and Communication. 11(3), 52–60.

[37] Sun, Z., Huo, Y., 2020. Intelligence without data. Global Journal of Computer Science and Technology C. 20(1), 25-35.

[38] Descartes, R., 1637. Discourse on the methods of the rightly conducting the reason, and seeking truth in the sciences. GlobalGrey: London.

[39] Taylor, T., 1801. The metaphysics of Aristotle. Davis, Wilks and Taylor: London.

[40] Kleene, S.C., 1952. Introduction to metamathematics. Ishi Press International: Jersey.

[41] Sun, Z., 2021. The age of metaintelligence: Competing in the digital world. PNG UoT BAIS. 6(8), 1–11.

[42] Aroraa, G., Lele, C., Jindal, M., 2022. Data analytics: Principles, tools and practices. BPB: New Dalhi, India.

[43] Sun, Z., Li, A., Liu, K., et al. (editors), 2008. Correspondence relationships among algebra, logic and intelligent systems. 4th National Conference of Logic System, Intelligent Science and Information Science (CLSIST2008); 2008 Oct 31–Nov 2; Guizhou. (in Chinese).

[44] Kantardzic, M., 2011. Data mining: Concepts, models, methods, and algorithms. Wiley & IEEE Press: Hoboken, NJ.

[45] Sun, Z., Wang, P.P., 2017. A mathematical foundation of big data. New Mathematics and Natural Computation. 13(2), 83–99. DOI: https://doi.org/10.1142/S1793005717400014

[46] Brooks, S., 2022. Business intelligence and analytics: Concepts, techniques and applications. Murphy & Moore Publishing: New York, NY.

[47] Sun, Z., Pambel, F., Wu, Z., 2022. The elements of intelligent business analytics: Principles, techniques, and tools. Handbook of research on foundations and applications of intelligent business analytics. IGI Global: Hershey. pp. 1–20.

[48] Lovell, M.C., 1983. Data mining. The Review of Economics and Statistics. 65(1), 1–12.

[49] Sun, Z., Huo, Y. (editors), 2019. A managerial framework for intelligent big data analytics. Proceedings of the 2nd International Conference on Software Engineering and Information Management; 2019 Jan 10–13; Bali, Indonesia. p. 152–156. DOI: https://doi.org/10.1145/3305160.3305211

[50] Magic Quadrant for Analytics and Business Intelligence Platforms [Internet] [cited 2019 Jul 7]. Available from: https://cadran-analytics.nl/wp-content/uploads/2019/02/2019-Gartner-Magic-Quadrant-for-Analytics-and-Business-Intelligence-Platforms.pdf

[51] Minelli, M., Chambers, M., Dhiraj, A., 2013. Big data, big analytics: Emerging business intelligence and analytic trends for today's businesses. John Wiley & Sons: Hoboken.

[52] Norusis, M.J., 1997. SPSS 7.5 guide to data analysis. Prentice Hall: Upper Saddle River.

[53] Sharda, R., Delen, D., Turban, E., 2018. Business intelligence and analytics: Systems for decision support (10th edition). Pearson: Boston, MA.

[54] Johnsonbaugh, R., 2013. Discrete mathematics (7th edition). Pearson Education Limited: London.

[55] Lang, S., 2002. Algebra, graduate texts in mathematics 211 (revised third edition). Springer-Verlag: New York.

[56] Mayer-Schönberger, V., Cukier, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt: Boston.

[57] The Age of Analytics: Competing in a Data Driven World [Internet]. Available from: https://www.cosmeticinnovation.com.br/wp-content/uploads/2017/01/MGI-The-Age-of-Analytics-Full-report.pdf

[58] Delen, D., Demirkan, H., 2013. Data, information and analytics as services. Decision Support Systems. 55(1), 359–363. DOI: https://doi.org/10.1016/j.dss.2012.05.044

Downloads

How to Cite

Sun, Z. (2024). Big Data 4.0: The Era of Big Intelligence. Journal of Computer Science Research, 6(1), 1–15. https://doi.org/10.30564/jcsr.v6i1.6054

Issue

Vol. 6 , Iss. 1 (January 2024)

Article Type

Article

License

Copyright © 2024 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC