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Data Integration

Data integration (also called information integration) is the process of consolidating data from a set of heterogeneous data sources into a single uniform data set (materialized integration) or view on the data (virtual integration). Data integration pipelines involve subtasks such as schema matching, table annotation, entity resolution, value normalization, data cleansing, and data fusion. Application domains of data integration include data warehousing, data lakes, and knowledge base consolidation. Surveys on Data integration:

Papers

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Showing 51–60 of 431 papers

TitleStatusHype
BayReL: Bayesian Relational Learning for Multi-omics Data IntegrationCode1
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph SummarizationCode1
From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer ResearchCode0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency—0
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction—0
The Cell Ontology in the age of single-cell omicsCode0
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