Critical Role of Data Transformation in Preprocessing: Methods, Algorithms, and Challenges

Critical Role of Data Transformation in Preprocessing: Methods, Algorithms, and Challenges

연구 분야: Databases

학회: International Conference on Model and Data Engineering

Data transformation is a crucial process in data preprocessing, converting raw data into a format suitable for analysis. This transformation is essential for harmonizing data from various sources, correcting inconsistencies, and preparing it for more advanced analytical tasks. The primary reasons for applying data transformation include improving data quality, enhancing compatibility across different systems, and enabling more accurate and efficient analysis. In data cleaning, transformation plays a key role by standardizing data, removing anomalies, and ensuring it is structured and formatted correctly. Transformation methods may involve scaling, normalization, and encoding, which help address challenges like inconsistent formats, redundant information, or missing values. Without these techniques, raw data would remain difficult to analyze effectively, leading to inaccurate or misleading conclusions. Data transformation not only improves compatibility between diverse data sources but also supports more advanced analyses like machine learning or statistical modeling, where structured data is crucial for algorithms to function properly. Our research reviews significant work in the field of data transformation, highlighting methodologies that have had a major impact on improving data quality. Techniques such as feature extraction, dimensionality reduction, and data type conversion are examined in detail, showing how they help refine datasets for more precise analysis. However, data transformation presents several challenges, including dealing with heterogeneous data from different platforms, managing large-scale datasets, and maintaining data integrity. Handling data from multiple sources requires careful planning and advanced tools, such as ETL pipelines, to ensure consistency. As data grows in size and complexity, these tasks become even more critical, requiring scalable solutions like distributed computing. This study aims to guide data scientists in selecting the most appropriate techniques for their projects, providing insights that are both practical and applicable to their data analysis tasks.