Machine learning-based business rule engine data transformation over high-speed networks

• Autorzy: Neelima Kenpi , Vasundra S.

Identyfikatory

DOI

10.24423/cames.472

• Języki publikacji: EN

Abstrakty

EN

Raw data processing is a key business operation. Business-specific rules determine howthe raw data should be transformed into business-required formats. When source datacontinuously changes its formats and has keying errors and invalid data, then the effectiveness of the data transformation is a big challenge. The conventional data extraction andtransformation technique produces a delay in handling such data because of continuousfluctuations in data formats and requires continuous development of a business rule engine.The best business rule engines require near real-time detection of business rule and datatransformation mechanisms utilizing machine learning classification models. Since data iscombined from numerous sources and older systems, it is challenging to categorize andcluster the data and apply suitable business rules to turn raw data into the business-required format. This paper proposes a methodology for designing ensemble machine learning techniques and approaches for classifying and segmenting registered numbersof registered title records to choose the most suitable business rule that can convert theregistered number into the format the business expects, allowing businesses to provide customers with the most recent data in less time. This study evaluates the suggested modelby gathering sample data and analyzing classification machine learning (ML) models todetermine the relevant business rule. Experimentation employed Python, R, SQL storedprocedures, Impala scripts, and Datameer tools.

Słowa kluczowe

EN

CRISP-DM; data mining algorithms; business rule; prediction; classification; machine learning; deep learning; AI design

PL

CRISP-DM; algorytmy eksploracji danych; reguła biznesowa; prognoza; klasyfikacja; nauczanie maszynowe; uczenie głębokie; projekt Sztucznej Inteligencji

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2023
• Tom: Vol. 30, no. 1
• Strony: 55--71
• Opis fizyczny: Bibliogr. 11 poz., rys., tab., wykr.

Twórcy

autor

Neelima Kenpi

• Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India

autor

Vasundra S.

• Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India

Bibliografia

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• 2. W. Kratsch, J. Manderscheid, M. Röglinger, J. Seyfried, Machine learning in business process monitoring: A comparison of deep learning and classical approaches used for outcome prediction, Business and Information Systems Engineering , 63 : 261–276, 2021, doi: 10.1007/s12599-020-00645-0.
• 3. A. Guha, D. Samanta, Hybrid approach to document anomaly detection: an application to facilitate RPA in title insurance, International Journal of Automation and Computing , 18 : 55–72, 2021, doi: 10.1007/s11633-020-1247-y.
• 4. S. Sun, Z. Cao, H. Zhu, J. Zhao, A survey of optimization methods from a machine learning perspective, IEEE Transactions on Cybernetics , 50 (8): 3668–3681, 2020, doi: 10.1109/TCYB.2019.2950779.
• 5. R. Nagar, Y. Singh, A literature survey on machine learning algorithms, Journal of Emerging Technologies and Innovative Research , 6 (4): 471–474, 2019, https://www.jetir.org/ view?paper = JETIR1904C77.
• 6. D. Ndirangu, W. Mwangi, L. Nderu, A hybrid ensemble method for multiclass classification and outlier detection, International Journal of Sciences: Basic and Applied Research , 45 (1): 192–213, 2019, https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/9904 .
• 7. N.A. Wahid, T.N. Adi, H. Bae, Y. Choi, Predictive business process monitoring – Remaining time prediction using deep neural network with entity embedding, [in:] The Fifth Information Systems International Conference: Elsevier Procedia Computer Science , Surabaya, Indonesia, July 23–24, Vol. 161, pp. 1080–1088, 2019, doi: 10.1016/j.procs.2019.11.219.
• 8. R. Makani, B.V.R. Reddy, Taxonomy of machine leaning based anomaly detection and its suitability, [in:] International Conference on Computational Intelligence and Data Science: Elsevier Procedia Computer Science , Ohio, USA, April 7–8, Vol. 132, pp. 1842–1849, 2018, doi: 10.1016/j.procs.2018.05.133.
• 9. J. Zhang, Advancements of outlier detection: A survey, ICST Transactions on Scalable Information Systems , 13 (1–3): 1–26, 2013, doi: 10.4108/trans.sis.2013.01-03.e2 .
• 10. N. Biswas, S. Chattapadhyay, G. Mahapatra, S. Chatterjee, K.C. Mondal, A new approach for conceptual ETL process modeling, International Journal of Ambient Computing and Intelligence , 10 (1): 30–45, 2009, doi: 10.4018/IJACI.2019010102 .
• 11. G.M.D. Sree, S. Vasundra, Vector-based classification prediction to geographical location, International Journal of Future Generation Communication and Networking , 13 (4): 4174– 4179, 2020.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).