- Tytuł:
- Machine learning-based business rule engine data transformation over high-speed networks
- Autorzy:
-
Neelima, Kenpi
Vasundra, S. - Powiązania:
- https://bibliotekanauki.pl/articles/38700094.pdf
- Data publikacji:
- 2023
- Wydawca:
- Instytut Podstawowych Problemów Techniki PAN
- Tematy:
-
CRISP-DM
data mining algorithms
business rule
prediction
classification
machine learning
deep learning
AI design
algorytmy eksploracji danych
reguła biznesowa
prognoza
klasyfikacja
nauczanie maszynowe
uczenie głębokie
projekt Sztucznej Inteligencji - Opis:
- 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.
- Źródło:
-
Computer Assisted Methods in Engineering and Science; 2023, 30, 1; 55-71
2299-3649 - Pojawia się w:
- Computer Assisted Methods in Engineering and Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł