Nov 22, 2024 · AI and ML are essential for data integration as they significantly enhance the process by automating and streamlining various tasks. Organizations can overcome data quality, scalability, and security challenges by leveraging AI and ML.

Sep 27, 2023 · AI (Artificial Intelligence) and Machine Learning (ML) are revolutionizing the field of data analytics by introducing automation, predictive capabilities, and advanced pattern recognition. Here's a more detailed explanation of their role in data analytics:

May 27, 2018 · This tutorial focuses on three aspects of the synergistic relationship between data integration and machine learning: (1) we survey how state-of-the-art data integration solutions rely on machine learning-based approaches for accurate results and effective human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications ...

machine learning-based approaches for accurate results and effec-tive human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant data for their analytics exercises, and (3) we discuss open research challenges and opportunities that span across data ...

Jan 29, 2025 · Through the development of a structured approach, this article elucidates the critical components necessary for successful implementation, including data preprocessing, algorithm selection, and performance evaluation.

Feb 1, 2022 · To improve the efficiency and accuracy of numerical simulations, we introduce Data Learning, which integrates DA and ML methods. This increases the reliability of prediction and reduces error by including information with a physical meaning from observed data.

Feb 22, 2024 · It examines how these technologies facilitate seamless data integration, improve data quality, and enable efficient data management processes. The study also highlights the implementation of...

We examine how AI-driven automation and ML-based predictive models enable seamless integration of heterogeneous data sources, reducing latency and increasing the scalability of analytics systems.

Jan 23, 2024 · We explore the possibilities of utilizing metadata obtained from data integration processes for improving the effectiveness, efficiency, and privacy of ML models. Towards this direction, we analyze ML training and inference over data silos.

How data from multiple sources are incorporated in a learning system is key step for a successful analysis. In this paper, we provide a comprehensive review on data integration techniques from a machine learning perspective. Conferences > 2015 IEEE International Confe...