Abstract
Knowledge society differentiates between information and knowledge, with a focus on generating, processing, transforming, and using information to create and apply knowledge. Within this context, knowledge acquisition and creation take precedence over the mere generation and consumption of information. Information technology and intelligent software systems play a crucial role in ensuring the efficiency of knowledge discovery, aligning with the knowledge society’s constant pursuit of innovation. The extraction of knowledge from dta increasingly relies on the incorporation of machine learning algorithms and artificial intelligence techniques, prompting a growing need for knowledge discovery frameworks that address escalating security concerns. In the realm of businesses and organizations aiming to offer comprehensive services and make data-driven decisions, the extraction of knowledge from intelligent systems becomes imperative. This goal is attainable by integrating knowledge discovery capabilities, thus facilitating the development of intelligent systems with knowledge discovery competence. This research introduces a meta level conceptual framework that empowers organizations looking to develop knowledge discovery systems, fostering knowledge discovery and simplifying the development of intelligent systems endowed with knowledge discovery capabilities.
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References
Bindé, J., Matsuura, K., UNESCO (eds.): Towards Knowledge Societies. UNESCO Publications (2005)
Sarker, I.H.: Machine learning: algorithms, real-world applications and Research Directions. SN Comput. Sci. 2(3) (2021). https://doi.org/10.1007/s42979-021-00592-x
Jansevskis, M., Osis, K.: Knowledge discovery frameworks and characteristics. Baltic J. Mod. Comput. (BJMC). (2023) [Submitted for publication]
Martinez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M.J., Flach, P.: CRISP-DM twenty years later: from data mining processes to data science trajectories. IEEE Trans. Knowl. Data Eng. 33(8), 3048–3061 (2021)
Rotondo, A., Quilligan, F.: Evolution paths for knowledge discovery and data mining process models. SN Comput. Sci. 1(2), 109 (2020)
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), 1299–1312 (2010)
Jansevskis, M., Osis, K.: User interaction and response-based knowledge discovery framework. Commun. Comput. Inf. Sci. (2023) [Submitted for publication]
Wang, J., Yang, Y., Wang, T., Sherrat, R.S., Zhang, J.: Big data service architecture: a survey. J. Internet Technol. 21(2), 393–405 (2020)
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), 38–49 (2019). https://doi.org/10.1504/ijbdi.2019.097399
Karunaratne, P., Karunasekera, S., Harwood, A.: Distributed stream clustering using micro-clusters on Apache Storm. J. Parallel Distrib. Comput. 108, 74–84 (2017). https://doi.org/10.1016/j.jpdc.2016.06.004
Bok, K., Oh, H., Lim, J., Pae, Y., Choi, H., Lee, B., Yoo, J.: An efficient distributed caching for accessing small files in HDFS. Cluster Comput. 20(4), 3579–3592 (2017). https://doi.org/10.1007/s10586-017-1147
Richa, B.: NoSQL vs SQL — Which Database Type is Better for Big Data Applications. https://analyticsindiamag.com/nosql-vs-sql-database-type-better-big-data-applications (2017). Last accessed 13 Mar 2023
General Data Protection Regulation (GDPR).: https://gdpr-info.eu/ (2016). Last accessed 8 May 2023
Jeren, A.: The impact of the GDPR on Big Data. Tech GDPR. https://techgdpr.com/blog/impact-of-gdpr-on-big-data (2020). Last accessed 9 May 2023
Schatz, D., Bashroush, R., Wall, J.: Towards a more representative definition of cyber security. J. Digit. Forensic Secur. Law. (2017). https://doi.org/10.15394/jdfsl.2017.1476
**n, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., Wang, C.: Machine learning and deep learning methods for cybersecurity. IEEE Access. 6, 35365–35381 (2018). https://doi.org/10.1109/ACCESS.2018.2836950
Vinayakumar, R., Alazab, M., Soman, K.P., Poornachandran, P., Al-Nemrat, A., Venkatraman, S.: Deep learning approach for intelligent intrusion detection system. IEEE Access. 7, 41525–41550 (2019). https://doi.org/10.1109/access.2019.2895334
Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Topics Comput. Intell. 2(1), 41–50 (2018). https://doi.org/10.1109/tetci.2017.2772792
Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing. 237, 350–361 (2017). https://doi.org/10.1016/j.neucom.2017.01.026
Technopedia Inc.: Knowledge Discovery. https://www.techopedia.com/definition/25827/knowledge-discovery-in-databases-kdd (2017). Last accessed 10 May 2023
Osei-Bryson, K.-M., Barclay, C. (eds.): Knowledge Discovery Process and Methods to Enhance Organizational Performance. CRC Press, Taylor & Francis Group (2015)
Ghezzi, C., Jazayeri, M., Mandrioli, D.: Fundamentals of Software Engineering. Prentice Hall (2003)
Rowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Sci. 33(2), 163–180 (2007). https://doi.org/10.1177/0165551506070706
Yousfi, S., Chiadmi, D., Rhanoui, M.: Smart big data framework for insight discovery. J. King Saud Univ. Comput. Inf. Sci. 34(10), 9777–9792 (2022). https://doi.org/10.1016/j.jksuci.2021.12.009
Rizvi, S., Zwerling, T., Thompson, B., Faiola, S., Campbell, S., Fisanick, S., Hutnick, C.: A modular framework for auditing IOT devices and networks. Comput. Secur. 132, 103327 (2023). https://doi.org/10.1016/j.cose.2023.103327
Khoda Parast, F., Sindhav, C., Nikam, S., Izadi Yekta, H., Kent, K.B., Hakak, S.: Cloud computing security: a survey of service-based models. Comput. Secur. 114, 102580 (2022). https://doi.org/10.1016/j.cose.2021.102580
Zheng, L., Wang, C., Chen, X., Song, Y., Meng, Z., Zhang, R.: Evolutionary machine learning builds smart education big data platform: data-driven higher education. Appl. Soft Comput. 136, 110114 (2023). https://doi.org/10.1016/j.asoc.2023.110114
SAS: Introduction to SEMMA. SAS Help Center (2017). https://documentation.sas.com/doc/en/emref/14.3/n061bzurmej4j3n1jnj8bbjjm1a2.htm
Chapman, P., Julian, C., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0: Step-by-step data mining guide. https://www.kde.cs.uni-kassel.de/wp-content/uploads/lehre/ws2012-13/kdd/files/CRISPWP-0800.pdf (2000)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Knowledge discovery and data mining: towards a unifying framework. In: KDD’96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 82–88 (1996)
Rollins, J.B.: Foundational methodology for data science. IBM Analytics. https://tdwi.org/~/media/64511A895D86457E964174EDC5C4C7B1.PDF
Severtson, R.B.: What is the Team Data Science Process? https://docs.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle (2021)
Moyle, S., Jorge, A. (2001). RAMSYS-A methodology for supporting rapid remote collaborative data mining projects.. https://www.researchgate.net/publication/247329752_RAMSYS-A_methodology_for_supporting_rapid_remote_collaborative_data_mining_projects
Gokalp, M.O., Kayabay, K., Akyol, M.A., Eren, P.E., Kocyigit, A.: Big data for industry 4.0: a conceptual framework. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 431–434 (2016)
Chen, M., Herrera, F., Hwang, K.: Cognitive computing: architecture, technologies and intelligent applications. IEEE Access. 6, 19774–19783 (2018)
Osman, A.M.S.: A novel big data analytics framework for smart cities. Fut. Gener. Comput. Syst. 91, 620–633 (2019)
Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Flach, P., Hernández-Orallo, J., Kull, M., Lachiche, N., Ramírez-Quintana, M.J.: CASP-DM: context aware standard process for data mining. ar**v. https://arxiv.org/abs/1709.09003 (2017)
Free writing AI assistance. Grammarly. https://www.grammarly.com/ (n.d.)
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Jansevskis, M., Osis, K. (2024). Securing the Future: The Role of Knowledge Discovery Frameworks. In: Sipola, T., Alatalo, J., Wolfmayr, M., Kokkonen, T. (eds) Artificial Intelligence for Security. Springer, Cham. https://doi.org/10.1007/978-3-031-57452-8_5
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