Abstract
The World Economic Forum in Davos in 2022 raised the issue of knowledge by describing the situation as follows: βIt could be that we are drowning in content, but starved of knowledge and therefore often fail to connect the dots to anticipate change before it becomes mainstream. With over four billion pieces of content being created each day, keeping abreast of all that is happening far exceeds our capacity to do so. The business models of social media organizations and news outlets have been increasingly focused on giving people more of what they like, leading to echo chamber effects and making it easy to lose sight of the big picture [10].β In recent decades a shift to the knowledge society has been acknowledged, characterized by its ability to identify, create, process, transform, disseminate and use information to generate and use knowledge for the development of individuals [2]. In such a society, intellectual capital is considered to be the most important indicator of wealth, ahead of assets. The acquisition, application and creation of knowledge is more important to the knowledge society than the creation and consumption of information. In regards to knowledge society requirements this paper presents a conceptual knowledge discovery framework: User Interaction and Response-based Knowledge Discovery Framework β UIS-KDF. The framework introduces a meta-level approach for knowledge discovery system design principles.
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References
Ackoff, R.L.: From data to wisdom: presidential address to ISGSR. J. Appl. Syst. Anal. 16, 3β9 (1989)
BindΓ©, J., Matsuura, K., (eds.): Towards Knowledge Societies. UNESCO Publications (2005)
Bok, K., et al.: An efficient distributed caching for accessing small files in HDFS. Clust. Comput. 20(4), 3579β3592 (2017). https://doi.org/10.1007/s10586-017-1147
Cao, L., Zhao, Y., Zhang, H., Luo, D., Zhang, C., Park, E.K.: Flexible frameworks for actionable knowledge discovery. IEEE Trans. Knowl. Data Eng. 22(9), 1299β1312 (2010). https://doi.org/10.1109/TKDE.2009.143
General Data Protection Regulation (GDPR) (2016). https://gdpr-info.eu/. Accessed 13 Mar 2023
Jansevskis, M., Osis, K.: State of knowledge discovery process models and frame-works. In: SOCIETY. TECHNOLOGY. SOLUTIONS. Proceedings of the International Scientific Conference, vol. 2, p. 14. (2022). https://doi.org/10.35363/via.sts.2022.81
Jeren, A.: The impact of the GDPR on big data. Tech GDPR (2020). https://techgdpr.com/blog/impact-of-gdpr-on-big-data. Accessed 13 Mar 2023
Karunaratne, P., Karunasekera, S., Harwood, A.: Distributed stream clustering using micro-clusters on Apache Storm. J. Parallel Distrib. Comput. 108, 74β84 (2017). https://doi.org/10.1016/j.jpdc.2016.06.004
Knowledge Society. International Encyclopedia of the Social Sciences. https://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/knowledge-society. Accessed 27 Feb 2023
Marshall, J., Mergenthaler, S.: These are the 3 ways knowledge can provide strategic advantage (2022). https://www.weforum.org/agenda/2022/01/this-is-how-knowledge-can-bring-you-strategic-advance. Accessed 12 Mar 2023
Oussous, A., Benjelloun, F.-Z., Ait Lahcen, A., Belfkih, S.: Big data technologies: a survey. J. King Saud Univ. Comput. Inf. Sci. 30(4), 431β448 (2018). https://doi.org/10.1016/j.jksuci.2017.06.001
Osei-Bryson, K.-M., Barclay, C. (eds.): Knowledge Discovery Process and Methods to Enhance Organizational Performance. CRC Press, Taylor & Francis Group (2015)
Osman, A.M.S.: A novel big data analytics framework for smart cities. Future Gener. Comput. Syst. 91, 620β633 (2019). https://doi.org/10.1016/j.future.2018.06.046
Richa, B.: NoSQL vs SQL β which database type is better for big data applications (2017). https://analyticsindiamag.com/nosql-vs-sql-database-type-better-big-data-applications. Accessed 13 Mar 2023
Schatz, D., Bashroush, R., Wall, J.: Towards a more representative definition of cyber security. J. Digit. Forensics Secur. Law (2017). https://doi.org/10.15394/jdfsl.2017.1476
Technopedia Inc. Knowledge discovery (2017). https://www.techopedia.com/definition/25827/knowledge-discovery-in-databases-kdd. Accessed 15 Mar 2023
Wang, J., Yang, Y., Wang, T., Sherrat, R.S., Zhang, J.: Big data service architecture: a survey. J. Internet Technology 21(2), 393β405 (2020)
Xin, Y., et al.: Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365β35381 (2018). https://doi.org/10.1109/ACCESS.2018.2836950
Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350β361 (2017). https://doi.org/10.1016/j.neucom.2017.01.026
Zhu, J.Y., Tang, B., Li, V.O.K.: A five-layer architecture for big data processing and analytics. Int. J. Big Data Intell. 6(1), 38β49 (2019). https://doi.org/10.1504/ijbdi.2019.097399
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Jansevskis, M., Osis, K. (2024). User Interaction and Response-Based Knowledge Discovery Framework. In: Lopata, A., GudonienΔ, D., ButkienΔ, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_8
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