Machine Learning-Based Business Rule Engine Data Transformation over High-Speed Networks

K. Neelima; S. Vasundra

doi:10.24423/cames.472

K. Neelima Jawaharlal Nehru Technological University Anantapur, India
S. Vasundra Jawaharlal Nehru Technological University Anantapur, India

DOI: http://dx.doi.org/10.24423/cames.472

Abstract

Raw data processing is a key business operation. Business-specific rules determine how the raw data should be transformed into business-required formats. When source data continuously 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 and transformation technique produces a delay in handling such data because of continuous fluctuations 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 data transformation mechanisms utilizing machine learning classification models. Since data is combined from numerous sources and older systems, it is challenging to categorize and cluster the data and apply suitable business rules to turn raw data into the businessrequired format. This paper proposes a methodology for designing ensemble machine learning techniques and approaches for classifying and segmenting registered numbers of registered title records to choose the most suitable business rule that can convert the registered 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 model by gathering sample data and analyzing classification machine learning (ML) models to determine the relevant business rule. Experimentation employed Python, R, SQL stored procedures, Impala scripts, and Datameer tools.

Keywords

CRISP, DM, data mining algorithms, business rules, prediction, classification, machine learning, deep learning, AI design, method,

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