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Optimizing Database Efficiency: Empowering Systems with Data Mining

Department of Electronics and Communications Engineering, Indira Institute of Technology and Sciences, Markapur, Andhra Pradesh, India.

Periodicity:July - September'2023
DOI : https://doi.org/10.26634/jit.12.3.20050

Abstract

Databases are critical for storing structured data, but deriving insights remains challenging. This paper investigates integrating classification, clustering, association rules, and anomaly detection within database architectures to enable intelligent analytics. A unified architecture is proposed along with an asynchronous incremental learning technique to efficiently handle dynamic data. Comprehensive experiments on diverse real-world datasets demonstrate 10–25% improvements in metrics like query latency, accuracy, and costs compared to conventional integration approaches. Emerging applications in multimedia, spatiotemporal, and IoT mining are discussed. The holistic convergence of multiple techniques is highlighted as the key innovation in progressing towards next-generation intelligent databases powered by analytics.

Keywords

Databases, Data Mining, Machine Learning, Knowledge Discovery, Database Performance, Database Architectures.

How to Cite this Article?

Eswaran, U. (2023). Optimizing Database Efficiency: Empowering Systems with Data Mining. i-manager’s Journal on Information Technology, 12(3), 32-38. https://doi.org/10.26634/jit.12.3.20050

References

[1]. Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer, New York (pp. 1-734).

[2]. Aggarwal, C. C. (2013). Outlier Analysis. Springer, New York, New York (pp. 1-34).

[3]. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58.

[4]. Eldawy, A., & Mokbel, M. F. (2015, April). Spatialhadoop: A mapreduce framework for spatial data. In 2015 IEEE 31st International Conference on Data Engineering (pp. 1352-1363). IEEE.

[5]. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.

[6]. Han, J., Pei, J., & Tong, H. (2022). Data Mining: Concepts and Techniques. Morgan Kaufmann, USA (pp. 1-752).

[7]. Hipp, J., Güntzer, U., & Nakhaeizadeh, G. (2000). Algorithms for association rule mining—A general survey and comparison. ACM Sigkdd Explorations Newsletter, 2(1), 58-64.

[8]. Jayaraman, B., & Evans, D. (2019). Evaluating differentially private machine learning in practice. In 28th USENIX Security Symposium (USENIX Security 19) (pp. 1895-1912).

[9]. Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons, New Jersey (pp. 1-534).

[10]. Maimon, O., & Rokach, L. (2005). Data Mining and Knowledge Discovery Handbook. Springer, New York (pp. 1-1285).

[11]. Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724-2743.

[12]. Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107.

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