Challenges in Data Integration for Different Data Types: A Systematic Review

Data integration is the process of combining information from different sources, including unstructured, semi-structured, and structured data. Custom integration strategies are required because each category presents unique processing and analysis challenges. The main challenge is effectively merging these various information formats. While semi-structured and unstructured data necessitate more complex strategies due to their variability and lack of established frameworks, structured data can be effectively managed with traditional techniques. Methods for integrating various data types are examined in this study. Semi-Structured data is processed using parsing tools like XML and JSON, structured data is managed with ETL and SQL, and unstructured data is processed using machine learning and natural language processing. Performance and accuracy were evaluated using real-world datasets. This evaluation will assist in identifying areas that need attention in upcoming research. This study was carried out by gathering 635 articles from digital libraries, specifically ScienceDirect, ACM, IEEE, Web of Science and Springer. Following an extensive evaluation, the 45 selected publications were relevant, well-regarded, and unique. Findings indicate that structured and semi-structured data can be effectively integrated using standard tools, whereas unstructured data necessitates more advanced methods. Hybrid methods that merge traditional and contemporary approaches proved to be the most effective, emphasizing the necessity for flexible integration frameworks.