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Data Integration for Data Science: Solutions and Still Open Problems
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DOI：10.1007/978-3-032-05727-3_21.png)
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Abstract
En 中文
This paper, accompanying a talk at the ADBIS 2025 Doctoral Consortium School, provides an overview of various data integration (DI) architectures - from virtual through physical to hybrid. It also presents an architecture for integrating stream data from robotic devices and introduces a novel concept for managing data source (DS) connectors. Additionally, the paper discusses selected current trends in applying machine learning to DI problems and outlines a few open research challenges. The insights presented in the talk and paper are drawn from our practical experience in data integration projects across the financial, IT, and intelligent farming sectors.
Keywords:
data integration
data science
data integration process
machine learning
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