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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 101–125 of 431 papers

TitleStatusHype
Assessing the Reproducibility of Machine-learning-based Biomarker Discovery in Parkinson's Disease—0
Control of Renewable Energy Communities using AI and Real-World Data—0
Corynebacterium glutamicum regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions—0
Prompt-Matcher: Leveraging Large Models to Reduce Uncertainty in Schema Matching Results—0
Clinical Trials Ontology Engineering with Large Language Models—0
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis—0
Crop Knowledge Discovery Based on Agricultural Big Data Integration—0
Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets—0
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data—0
Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance—0
An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic—0
A High Precision Pipeline for Financial Knowledge Graph Construction—0
Crowd Safety Manager: Towards Data-Driven Active Decision Support for Planning and Control of Crowd Events—0
Current and future directions in network biology—0
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches—0
AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Data Fusion by Matrix Factorization—0
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder—0
CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies—0
Data Integration Framework for Virtual Reality Enabled Digital Twins—0
Data integration in systems genetics and aging research—0
A machine-compiled macroevolutionary history of Phanerozoic life—0
Data Integration with High Dimensionality—0
An Ontology for Defect Detection in Metal Additive Manufacturing—0
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